Report pulled 11/21/25. For planning purposes only. Courses subject to change.

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CROSS-LISTED COURSES

This course explores the transformative power of digital twins in harnessing data-driven insights and improving decision making using predictive analytics. In this course, students will learn principles and applications at the dynamic intersection of digital twin technology, data analysis, and applied machine learning (ML) to support predictive analytics across engineering areas within research, industry, business, and government. The technical course content will progress data analysis, to inference, to applied ML in order to demonstrate the process of collecting, cleaning, interpreting, transforming, exploring, and analyzing data generated by digital twin models. Using this process, students will learn to extract pertinent information, communicate insights, and support decision making based on predictions of how engineered systems perform under future scenarios. The advantages of using visualization techniques to explore data and communicate outcomes will be highlighted throughout the course. This course will include lectures that embed concepts from across the AI broadly to support applied ML along the predictive analytics lifecycle. Select lectures providing additional breadth will include the implications of ethics and social justice in AI, how to select appropriate computing platforms for predictive analytics, and data management. By the end of the course, students are expected to be able to plan, design, and implement empirical research projects using statistical, computational, and quantitative applied ML techniques, predict system response to support data-driven decision-making using digital twins, and discuss the ethics of AI-driven decision making such as bias.

Weiss, M (mdweiss)

This course explores the transformative power of digital twins in harnessing data-driven insights and improving decision making using predictive analytics. In this course, students will learn principles and applications at the dynamic intersection of digital twin technology, data analysis, and applied machine learning (ML) to support predictive analytics across engineering areas within research, industry, business, and government. The technical course content will progress data analysis, to inference, to applied ML in order to demonstrate the process of collecting, cleaning, interpreting, transforming, exploring, and analyzing data generated by digital twin models. Using this process, students will learn to extract pertinent information, communicate insights, and support decision making based on predictions of how engineered systems perform under future scenarios. The advantages of using visualization techniques to explore data and communicate outcomes will be highlighted throughout the course. This course will include lectures that embed concepts from across the AI broadly to support applied ML along the predictive analytics lifecycle. Select lectures providing additional breadth will include the implications of ethics and social justice in AI, how to select appropriate computing platforms for predictive analytics, and data management. By the end of the course, students are expected to be able to plan, design, and implement empirical research projects using statistical, computational, and quantitative applied ML techniques, predict system response to support data-driven decision-making using digital twins, and discuss the ethics of AI-driven decision making such as bias.

Weiss, M (mdweiss)

Due to concerns of climate change and global warming, electrical vehicles (EV) are rapidly replacing fossil fuel based EVs. The development of electrical transport in various parts of the world is hampered by lack of capacity and technical know-how. Although Digital technologies are maturing, there is a lack of technical knowledge, skills and capacity in the electrical systems and controls This master’s level 12-unit course is aimed at filling this gap. Content will include: overview of electric vehicles, key components, battery pack, power devices and converters, electric traction motors, thermal system, vehicle speed and torque control, communication and diagnostics, future developments.

Tennakoon, S (stennako)

With the advent of power electronics, control and communication systems and internet technologies, the grid connected and stand-alone electricity supply systems can be made smart and flexible by the application of power electronics. This is particularly relevant for the increasing penetration of embedded generation due to the proliferation of renewable energy systems based on solar, wind, mini and micro hydro and wave in addition to the Diesel and gas generators. This course is designed to produce engineers equipped with the necessary knowledge and skills to design, commission and operate such systems. Content includes: high voltage switches, both thyristor and IGBT based; reactive power compensation: thyristor-controlled reactor (TCR), thyristor switched capacitor (TSC), static var compensator (SVC), STATCOM, series and shunt compensation; high voltage DC transmission (HVDC): HVDC converters (both thyristor-based and voltage source converter-based), multiterminal HVDC, DC grids; grid integration of renewable energy sources: wind power on shore and off shore, solar, energy storage, application to weak systems, black start; stand-alone grid systems: PV and hydro based, energy storage, rural electrification and city applications, business models. Completion of a previous course in power electronics is recommended.

Tennakoon, S (stennako)

With the advent of power electronics, control and communication systems and internet technologies, the grid connected and stand-alone electricity supply systems can be made smart and flexible by the application of power electronics. This is particularly relevant for the increasing penetration of embedded generation due to the proliferation of renewable energy systems based on solar, wind, mini and micro hydro and wave in addition to the Diesel and gas generators. This course is designed to produce engineers equipped with the necessary knowledge and skills to design, commission and operate such systems. Content includes: high voltage switches, both thyristor and IGBT based; reactive power compensation: thyristor-controlled reactor (TCR), thyristor switched capacitor (TSC), static var compensator (SVC), STATCOM, series and shunt compensation; high voltage DC transmission (HVDC): HVDC converters (both thyristor-based and voltage source converter-based), multiterminal HVDC, DC grids; grid integration of renewable energy sources: wind power on shore and off shore, solar, energy storage, application to weak systems, black start; stand-alone grid systems: PV and hydro based, energy storage, rural electrification and city applications, business models. Completion of a previous course in power electronics is recommended.

Tennakoon, S (stennako)

This course offers the hands-on training on how to apply modern CAD and CAE software tools to engineering design, analysis and manufacturing. In the first section, students will learn through 7 hands-on projects how to model complex free-form 3D objects using commercial CAD tools. In the second section, students will learn through 7 hands-on projects how to simulate complex multi-physics phenomena using commercial CAE tools. Units: 12 Format: 2 hrs. Lec., 2 hrs. computer lab

Ozel, S (sozel)

This course offers the hands-on training on how to apply modern CAD and CAE software tools to engineering design, analysis and manufacturing. In the first section, students will learn through 7 hands-on projects how to model complex free-form 3D objects using commercial CAD tools. In the second section, students will learn through 7 hands-on projects how to simulate complex multi-physics phenomena using commercial CAE tools. Units: 12 Format: 2 hrs. Lec., 2 hrs. computer lab

Ozel, S (sozel)

This course offers the hands-on training on how to apply modern CAD and CAE software tools to engineering design, analysis and manufacturing. In the first section, students will learn through 7 hands-on projects how to model complex free-form 3D objects using commercial CAD tools. In the second section, students will learn through 7 hands-on projects how to simulate complex multi-physics phenomena using commercial CAE tools. Units: 12 Format: 2 hrs. Lec., 2 hrs. computer lab

Ozel, S (sozel)

Organizations are increasingly adopting formal project management techniques to successfully initiate, plan, execute, monitor, control, and close out projects. In this course, students will learn many project management tools which are commonly applied in industry. Students will incorporate these tools into a documented plan for a project on which they are currently working or have previously completed. The project plan will address the ten knowledge areas of project management, including the management of project integration, scope, time, cost, quality, human resources, communications, risk, procurement, and stakeholders. Students will also assume the role of a project manager, functional/line manager, or engineer in a project management simulation. Real world constraints, challenges, and incentives will be applied. Additional special topics in project management will be discussed based on student interest, which may include lean, agile, and industry-specific approaches, as well as project management certification.

Instructor TBA

This course introduces fundamental machine learning and artificial intelligence techniques useful for engineers working on data-intensive problems. Topics include: Probability and Bayesian learning, generative and discriminative classification methods, supervised and unsupervised learning, neural networks, support vector machines, clustering, dimensionality reduction, regression, optimization, evolutionary computation, and search. The lectures emphasize the theoretical foundations and the mathematical modeling of the introduced techniques, while bi-weekly homework assignments focus on the implementation and testing of the learned techniques in software. The assignments require knowledge of Python including text and image input/output, vector and matrix operations, simple loops, and data visualization. Students must have undergraduate level experience with linear algebra and vector calculus. 24-887 is a remote version of the in-person course 24-787 and is exclusively for students enrolled in the AI / ML certificate program.

Wang, L (liweiw)

This course introduces fundamental machine learning and artificial intelligence techniques useful for engineers working on data-intensive problems. Topics include: Probability and Bayesian learning, generative and discriminative classification methods, supervised and unsupervised learning, neural networks, support vector machines, clustering, dimensionality reduction, regression, optimization, evolutionary computation, and search. The lectures emphasize the theoretical foundations and the mathematical modeling of the introduced techniques, while bi-weekly homework assignments focus on the implementation and testing of the learned techniques in software. The assignments require knowledge of Python including text and image input/output, vector and matrix operations, simple loops, and data visualization. Students must have undergraduate level experience with linear algebra and vector calculus. 24-887 is a remote version of the in-person course 24-787 and is exclusively for students enrolled in the AI / ML certificate program.

Instructor TBA

Barati Farimani, A (afariman)

Mozisek, K (kmozisek)

Mozisek, K (kmozisek)

Mozisek, K (kmozisek)

MIIPS Online & Certificate Students Only - Product Design Innovation Certificate This course is an introduction to design principles for product development and instruct students in techniques and applications for tangible products. During the course, students will learn about the design process and the steps designers take from an understanding of user needs to the creation of a fully considered solution that meets those needs and delights the user. Building on the general principles introduced in the course, the course will give students the opportunity to apply their learning in the context of tangible products (research, sketching, model making, user testing, and presentation). Students experience the use of traditional design skills (drawing, mockups, and model making) in the visualization and representation of design concepts and solutions. This course not only introduces design innovation but also provides training in a physical product context so that students can hone skills and techniques needed to visualize and represent product concepts efficiently. The relationship among design, product development, and business is explored with class projects, readings, discussions, and the analysis of artifacts and process. Through case histories, lectures, and a variety of hands-on exercises, students are exposed to design thinking and practice. Students will learn about and practice techniques that include: 1. Conducting observational research, 2. Analyzing information to inform team brainstorming, 3. Planning projects, 4. Developing concept strategies, 5. Generating ranges of solutions especially via methods of early prototyping and testing through the use of interactive and experiential mock-ups, 6. Selecting and refining concepts. Students will synthesize these techniques to solve a product design challenge in the course. Certain assignments will be completed as individuals and other assignments in teams.

Zlotnikov, S (susannaz)

MIIPS Online & Certificate Students Only - Product Design Innovation Certificate This course will define and study services, experiences, and related systems. Students will also learn the basics of designing services and experiences. Innovators who focus on services and experiences create new offerings for businesses with a primary focus on the quality of the human interactions and experience that are often engendered in the context of functional and/or tangible products, meaning that this course will push students to consider holistic "product" offerings that span UI/UX, physical products, and human activity. In this course students will first study the nature of services and experiences and then work in small project teams to analyze leading designed solutions as well as to create new ones. Service and experience design frameworks will also be used both for the analysis of existing offerings as well as to propose and innovative solutions. The learning will take place via lectures, studio projects, and verbal and written exposition. Students will be working in familiar and unfamiliar forms including concepts for products, documents, events, spaces, activities, scripts, and software. Classwork will be done individually and in teams. By the end of this course, students should be able to: Easily distinguish and shift between different perspectives on the same design problem space, leverage service and experience innovation frameworks to explain how an offering unfolds for people, speak articulately about offerings that are made up of systems of products, services and other components.

Zlotnikov, S (susannaz)

MIIPS Online & Certificate Students Only - Methods & Tools for Product Innovation Certificate This course covers early stages of a product innovation process: identifying, understanding, and then conceptualizing a product opportunity. The course presents fundamental tools to assess trends, identify opportunities, identify and uncover the value proposition of key stakeholders, articulate the value proposition, define product requirements and conceptualize solutions. Because innovation insights and ideas are new and can be abstract without additional effort, it is important that students learn how to make ideas more concrete via visual communication techniques. As such, communication of work and findings are core to this course. We will cover the following: 1. Industrial Design Sketching, 2. Information visualization & dashboards, 3. Graphic User interface design, 4. Executive Summary and Pitch Decks, 5. Visual Brand Language, Templates and Styling 6. Visual Explanations, and 7. Storyboarding and making simple videos. Weekly visual communication assignments will allow students to develop their communication skills throughout the course. The course will revolve around opportunities for product innovation. Students will implement the innovation process that they learn in projects, leading to a final deliverable of a product concept that they communicate with both text and visual techniques.

Instructor TBA

MIIPS Online & Certificate Students Only - Methods & Tools for Product Innovation Certificate This course will teach the basic methods of user research, including one-on-one interviewing and ethnographic techniques. To allow students to master certain skills, the students will dive deeper into one method. Students apply the basic principles of ethnography in a project as a participant observer in both digital settings and in traditional settings. Students will plan the research, collect data, analyze and synthesize what was learned and present a research report that identifies not only what was observed but also interpret its meaning and make indications about opportunities to innovate with new offerings. Although the course will focus on qualitative and primary research, the benefits of quantitative and secondary research will also be addressed. The course includes lectures and discussions, along with readings and research assignments.

Instructor TBA

MIIPS Online & Certificate Students Only - Course for New Product Management Certificate This course focuses on the strategies and methods for building, leveraging, defending, and sustaining inspired new products and brands. A successful new product has many similarities to a successful new business, so this class will also cover a broad set of business management concepts from the various functional business areas, motiving them in context of successful product development and launch. The course also emphasizes pricing strategies and tactics, recognizing both the importance of pricing but also the recognizing the close link of pricing to fundamental business principles. We will discuss the actions required to bring a product to market, including understanding your target audiences' needs, values and lifestyles and the key elements of a launch plan. We will apply concepts and discuss the span of products: consumer and B2B, products and services, digital and physical.

Instructor TBA

MIIPS Online & Certificate Students Only - New Product Management Certificate This course explores the concepts, roles and responsibilities associated with both product management and brand management, also covering how strategy and business models intertwine to shape the nature and success of a product and business. Tools and methods will be introduced that allow a business to better understand and define itself and recognize its position in the market environment. The course will also cover planning, development and marketing tools that product and brand managers use to make decisions on how to deliver the expected value to customers and stakeholders and differentiate itself from competition. These tools will help you address common strategic, as well as tactical, challenges across the product lifecycle to make a product or service successful. In addition to covering theory and applications, the course will use a business simulation to help students to understand how the functional areas tie together. The course will cover a variety of contexts: corporate and entrepreneurial, for-profit and not-for-profit, products and services, business to business and consumer products, digital and physical products. Further, the course will build on your knowledge of marketing, engineering, accounting, and manufacturing, showing how product managers and brand managers work cross-functionally and play critical leadership functions to make products and services successful.

Instructor TBA

MIIPS Online Students Only - Professional Practice of Product Innovation This course focuses on team-­based product development that integrates engineering, business, and design disciplines, focusing not solely on the tasks but also the professionalism that is important for working on future projects in which there would be a client. The course consists of four phases including identifying, understanding, conceptualizing and introducing a product opportunity. Students learn methods to research the needs, wants and desires of a market opportunity, define product specifications, conceptualize products to meet the users' needs and desires and refine the most promising concept. Students will hone their skills of formulating a hypothesis, supporting it with evidence, and logically presenting their conclusions.  The project will result in a resolved form, functional design, and marketing plan. That said, the goal is not only to "build gears" or "write code" or "develop a business plan" but rather for students to develop a deeper understanding how those methods can impact the success of innovation projects, developing their ability to critically assess how theoretical methods and principles impact a practical innovation challenge. The course also emphasizes communication of the project, through multiple presentations and reports.

Ayoob, E (fudge)

This course will examine the role that social media and technology have had on conflict at multiple levels, both between and within nations. Interconnectedness has expanded dramatically and continues to expand, allowing the formerly disconnected¿individuals with shared political views, states and diaspora populations¿to be intimately connected. The Arab Spring uprisings were significantly influenced by the use of cell phones, social media, and text-messaging as organizing tools. Insurgent groups like the Islamic States harnessed the power of social media and emerging technologies, and now extremist groups in the US and Europe are using Twitter, YouTube, Telegram and other social media platforms to their advantage. Information war is a critical factor in Russia's invasion of Ukraine, as both sides work to support/exploit kinetic warfare. Social media is used both to recruit for and fund violent extremism, while the internet has become a channel for radicalizing individuals into violent ideologies. Loss of trust in media and institutions, and the proliferation of mis/disinformation, conspiracy theories, and malign information operations over social media has introduced a new dimensions to conflict and relations between individuals, small groups, non-state actors, and nation-states.

Marcellino, W (wmarcell)

This course is only for students completing the Foundations of Data Science Online Graduate Certificate. Students will learn how to understand fundamental terminology and techniques so they may be correctly applied in future data analysis situations. Introducing the theoretical aspects of probability and statistical inference, topics include basic probability, random variables, univariate and bivariate probability distributions, statistics, likelihood, point and interval estimation, hypothesis testing, and the frameworks underlying linear and logistic regression and Naive Bayes. Mathematical details are supplemented with computer-based examples and exercises (e.g. visualization and simulation).

Instructor TBA

This course is only for students completing the Foundations of Data Science Online Graduate Certificate. This course is designed to introduce how to approach and analyze data, topics include data input/output, basic data processing, exploratory data analysis, clustering, common regression and classification models (including those of classical statistics and of machine learning), and experimental design. Students will practice using these methods on real-world data and will subsequently apply them when analyzing data in the program's Data Science Capstone course.

Instructor TBA

This course is only for students completing the Foundations of Data Science Online Graduate Certificate. Students work with real-world data to apply the skills and knowledge acquired throughout the program. Supported by subject matter experts, you will have the opportunity to practice synthesizing and communicating results in a clear and concise manner.

Instructor TBA

Mozisek, K (kmozisek)

THIS COURSE IS FOR THE AI CERTIFICATE PROGRAM ONLY. NO OTHER STUDENTS WILL BE REGISTERED.

Rao, A (anandr2)

Instructor TBA

February 1, 2 and 9, 2025. Consumer Analytics is a growing field that spans industry boundaries, but is rapidly evolving within the health care industry. Patient utilization history and claims data are being used to drive population health programs, website search histories are helping public health officials predict disease outbreaks, and the constant flow of data from wearable devices are reminding us to stand, exercise, and mediate. Health Care consumers now have more access to information - pharmaceutical companies are engaged in direct-to-consumer marketing, patients can look up quality outcomes for their provider, and medication instructional videos are now on YouTube. This micro mini course will highlight the data sources and insights used by providers, payers and tech companies to understand and influence consumer behavior. Students will gain insight into the descriptive, predictive, and prescriptive analytic methodologies and explore specific use cases alongside industry experts. Topics will also include the social science of behavior change and privacy and ethical implications of collecting and utilizing health data.

Pawar, M (mpawar)

THIS COURSE IS FOR THE AI CERTIFICATE PROGRAM ONLY. NO OTHER STUDENTS WILL BE REGISTERED.

Dzombak, R (rdzombak)

This course introduces students to leadership via three learning frameworks: models, practice and reflection. Using guest lectures, readings and videos, and small group discussions, we will define leadership and provide applicable frameworks for leadership practice. Students will be able to experiment and practice different skills and styles in a safe environment where they can receive useful feedback. To help students pursue their own personal path of leadership development, the focus is on formulating personal goals, models and activities that sustain this development over the course of their careers.

Lassman, D (dlassman)

Steier, D (steier)

Databases systems are central to most organizations' information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems - understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that is most often used these days, and is the primary focus of this course. Further, to provide students with opportunity to apply the knowledge they learn from the lectures, various homework assignments, SQL assignments, and a database implementation project will be given.

Smith, J (smithj)

Large-scale software development has been described as one of the most difficult of human undertakings. This course examines the reasons for the inherent complexity of software construction, and presents structured methods to deal effectively with it. The course will focus on the object-oriented approach for analysis and design. Students will gain an appreciation of the difference between writing programs and doing analysis and design. Problem formulation and decomposition (analysis) and solution building (design) will be covered. Students will work in small groups, each group having the responsibility for analysis, design and implementation of a software system. Case tools will be used in several stages of the development process. Knowledge of an Object-Oriented language such as Java or C++ is a pre-requisite for this course.

Cois, C (cacois)

The IS project course provides students an exciting opportunity to apply skills they develop in the classroom to a problem from a real world context. In doing so, students begin to make the transition from their academic world to the environments in which they will work once they graduate. In these environments, the challenges of team building, resource development, client relations, and pressing deadlines are as real and important as the technical and managerial components of any task. The project is a semester-long intensive team-based experience. A typical project includes design and development of an information system for an external client - often a corporation or public agency. The project deliverable may result in a final report, prototype, or finished system. <a href='http://www.heinz.cmu.edu/academic-resources/course-results/course-details/index.aspx?cid=307'>...Read More</a>

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Instructor TBA

The IS project course provides students an exciting opportunity to apply skills they develop in the classroom to a problem from a real world context. In doing so, students begin to make the transition from their academic world to the environments in which they will work once they graduate. In these environments, the challenges of team building, resource development, client relations, and pressing deadlines are as real and important as the technical and managerial components of any task. The project is a semester-long intensive team-based experience. A typical project includes design and development of an information system for an external client - often a corporation or public agency. The project deliverable may result in a final report, prototype, or finished system. <a href='http://www.heinz.cmu.edu/academic-resources/course-results/course-details/index.aspx?cid=307'>...Read More</a>

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Instructor TBA

The IS project course provides students an exciting opportunity to apply skills they develop in the classroom to a problem from a real world context. In doing so, students begin to make the transition from their academic world to the environments in which they will work once they graduate. In these environments, the challenges of team building, resource development, client relations, and pressing deadlines are as real and important as the technical and managerial components of any task. The project is a semester-long intensive team-based experience. A typical project includes design and development of an information system for an external client - often a corporation or public agency. The project deliverable may result in a final report, prototype, or finished system. <a href='http://www.heinz.cmu.edu/academic-resources/course-results/course-details/index.aspx?cid=307'>...Read More</a>

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Instructor TBA

Instructor TBA

Across the board, IT managers in government and industry are concerned with regulatory compliance. This course is designed to introduce students to key Information Security industry and government policies, regulations and standards. The course is structured to familiarize students with base standards, like NIST, and more specific regulatory requirements, and to help students understand how those requirements are met, using frameworks, controls and training. The goal of this course is provide students with an understanding of how to develop an organization's information security policy and procedures to comply with government and industry regulations. This course is for graduate students seeking to work or manage an information security and privacy department.

Haller, J (jhaller)

The ability to secure information within a modern enterprise is a growing challenge. Threats to information security are global, persistent, and increasingly sophisticated. Long gone are the days when managers could hope to secure the enterprise through ad hoc means. Effective information security at the enterprise level requires participation, planning, and practice. Fortunately, the information security community has developed a variety of resources, methods, and best practices to help modern enterprises address the challenge. However, employing these tools demands a high degree of commitment, understanding, and skill¿attributes that must be sustained through constant awareness and training. An essential part of the information security plan is cyber security policy - this includes the written plans for how the enterprise IT assets will be protected. This course provides students with information on the origin of cyber security policy, governance structures for policy creation, selection and implementation of policy, and audit and control functions to ensure compliance and efficacy. Students will be exposed to the national and international policy and legal considerations related to cybersecurity and cyberspace such as privacy, intellectual property, cybercrime, homeland security (i.e., critical infrastructure protection) and cyberwarfare, and the organizations involved in the formulation of such policies. Broader technology issues also are discussed to demonstrate the interdisciplinary influences and concerns that must be addressed in developing or implementing effective national cybersecurity laws and policies.

Haller, J (jhaller)

This course examines risk management practices and principles to improve information security.  The course provides education on information security risk identification, evaluation, and related response decisions given resource constraints. Students will learn foundational concepts in risk management and economic valuation and will be introduced to standard risk management approaches for identifying, analyzing, responding, and monitoring risks. Both qualitative and quantitative approaches will be examined.

Tucker, B (brettt)

The course provides an overview of computer programming concepts and object-oriented thinking using the Java programming language. Students will be introduced to general programming concepts such as loops and recursions as well as the specific object-oriented themes of methods, classes, and inheritance. The goal is for the student to cultivate an appreciation and understanding of the impact of these concepts and themes on the management of large-scale software development projects. "This free CMU OLI course is recommended prior to 95-807: https://oli.cmu.edu/courses/introduction-to-programming-in-java-o-f/."

Dwivedi, N (ndwivedi)

The main purpose of this course is to provide students with a basic understanding of supervised and unsupervised machine learning models. This course will face students with real examples and real-world data, as an increasing number of organizations nowadays collect data to support their decision-making process. Learning from data can enable us to better: evaluate sales decisions, make a medical diagnosis, monitor the reliability of IT systems, p market segmentation, improve the success of marketing campaigns, and much, much more. NOTE: This course is primarily intended and designed for MSIT students. Other Heinz students in full-time programs should enroll in 94-819 Data Analytics with Tableau and will not be permitted to enroll in this course.

Instructor TBA

Companies, governments, and other organizations now collect massive amounts of data such as text, images, audio, and video. How do we turn this heterogeneous mess of data into actionable insights? A common problem is that we often do not know what structure underlies the data ahead of time, hence the data often being referred to as "unstructured". This course takes a practical approach to unstructured data analysis via a two-step approach: We first examine how to identify possible structure present in the data via visualization and other exploratory methods. Once we have clues for what structure is present in the data, we turn toward exploiting this structure to make predictions. Many examples are given for how these methods help solve real problems faced by organizations. Along the way, we encounter many of the most popular methods in analyzing unstructured data, from modern classics in manifold learning, clustering, and topic modeling to some of the latest developments in deep neural networks for analyzing text, images, and time series. We will write lots of Python code and also work with Amazon Web Services (AWS) for cloud computing.

Instructor TBA

Simko, M (msimko2)

The objective of the Quantitative Methods II course is to review mathematical concepts, concentrating primarily on an algebraic and statistical foundation, so as to prepare candidates with an improved, practice-based, applicable understanding of such concepts before applying them within the statistics course that is required as part of their program's academic core. Upon completion of this course, the candidate will be able to: 1. Illustrate fundamental statistical concepts 2. Solve and interpret statistical operations 3. Define and apply basic statistical theory 4. Understand and explain applications of statistical analysis These objectives will be assessed via assignments and a final examination.

Instructor TBA

In this course students will be introduced to Affordable Housing Policy and Finance in the U.S. Context. Approximately half of each class session will be spent describing local, state, and federal housing policies and discussing the implications (both positive and negative) of these policies in urban settings. The students will read two books to be used as aids for class discussion. The books are The Color of Law by Richard Rothstein and Evicted by Matthew Desmond. Additionally, there may be one or two occasions where a guest speaker presents a specific policy issue. - The remaining half of each class session will be spent learning the financial computations used by practitioners of housing development. In other words, how are new affordable housing developments financed when the developer knows that the rents will be less than what he/she could receive on the private market? The course will cover the basic principles of both market-rate and affordable housing finance. The instructor will also teach the fundamentals of the Low-Income Housing Tax Credit. By the end of the course, the students will be expected to know how to calculate Low-Income Housing Tax Credits and use them to generate equity in affordable housing developments. - The course is for students who may want to work for local, state, and/or the federal government directly impacting issues related to affordable housing. This course is also for students who would like to work for an affordable housing developer, a real estate practitioner, and/or is just interested in the topic of affordable housing. - There are no prerequisites for the course.

Smith, J (jesmith)

This course gives students an overview of Cloud Computing, which is the delivery of computing as a service over a network, whereby distributed resources are rented, rather than owned, by an end user as a utility. Students will study its enabling technologies, building blocks, and gain hands-on experience through projects utilizing public cloud infrastructures. Cloud computing services are widely adopted by many organizations across domains. The course will introduce the cloud and cover the topics of data centers, software stack, virtualization, software defined networks and storage, cloud storage, and programming models. We will start by discussing the clouds motivating factors, benefits, challenges, service models, SLAs and security. We will describe several concepts behind data center design and management, which enable the economic and technological benefits of the cloud paradigm. Next, we will study how CPU, memory and I/O resources, network (SDN) and storage (SDS) are virtualized, and the key role of virtualization to enable the cloud. Subsequently, students will study cloud storage concepts like data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage using HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift as case studies. Finally, students will study the MapReduce, Spark and GraphLab programming models. Students will work with Amazon Web Services and Microsoft Azure, to rent and provision compute resources and then program and deploy applications using these resources. Students will develop and evaluate scaling and load balancing solutions, work with cloud storage systems, and develop applications in several programming paradigms. 15619 students must complete an extra team project which entails designing and implementing a cost- and performance-sensitive web-service for querying big data.

Sakr, M (msakr); Goldstein, S (sethg)

This course gives students an overview of Cloud Computing, which is the delivery of computing as a service over a network, whereby distributed resources are rented, rather than owned, by an end user as a utility. Students will study its enabling technologies, building blocks, and gain hands-on experience through projects utilizing public cloud infrastructures. Cloud computing services are widely adopted by many organizations across domains. The course will introduce the cloud and cover the topics of data centers, software stack, virtualization, software defined networks and storage, cloud storage, and programming models. We will start by discussing the clouds motivating factors, benefits, challenges, service models, SLAs and security. We will describe several concepts behind data center design and management, which enable the economic and technological benefits of the cloud paradigm. Next, we will study how CPU, memory and I/O resources, network (SDN) and storage (SDS) are virtualized, and the key role of virtualization to enable the cloud. Subsequently, students will study cloud storage concepts like data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage using HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, and Swift as case studies. Finally, students will study the MapReduce, Spark and GraphLab programming models. Students will work with Amazon Web Services and Microsoft Azure, to rent and provision compute resources and then program and deploy applications using these resources. Students will develop and evaluate scaling and load balancing solutions, work with cloud storage systems, and develop applications in several programming paradigms. 15619 students must complete an extra team project which entails designing and implementing a cost- and performance-sensitive web-service for querying big data.

Sakr, M (msakr); Goldstein, S (sethg)

Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.

Brown, R (ralf)

Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.

Brown, R (ralf)

Rose, C (cp3a)

This course provides an introduction to foundational concepts, learning material, and projects related to the three core areas of Data Science: Computing Systems, Analytics, and Human-Centered Data Science. Students completing this class will be prepared for further graduate education in Data Science and/or Artificial Intelligence. Students acquire skills in solution design (e.g., architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g., Natural Language Processing, Computer Vision) and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks. This course is a remote course which is designed specifically for remote students in a certificate program.

Rose, C (cp3a)

Deep learning is a subfield of AI that has lately taken the world by storm. Deep learning systems have been shown to able to recognize speech almost as well as humans, recognize images better than humans, read the web and answer questions, learn on their own to play games, beat humans at the toughest games like go and even speak more clearly than a human can. Deep learning currently dominates research in a variety of scientific areas, including text and language processing, data mining, speech processing, computer vision, robotics, and AI. Deep learning based products and services dominate the market in many areas. Whether youre using Google or social media, buying a plane ticket, browsing an online retailer, investing in stocks, or hailing an Uber, you are interacting with a deep learning system. Knowledge of deep learning is considered a valuable asset, and sometimes even essential, in the employment market. So what exactly is this mysterious beast? In this course we will study the basics of deep learning systems, starting from their humble beginnings as attempts to understand human cognition, their adolescence as artificial neural networks, leading on to the current complex systems that can perform astounding tasks. Students will learn both the underlying principles through a series of 13 lectures, and to actually implement and manipulate these systems for various tasks through a series of lab exercises.

Ramakrishnan, B (bhikshar); Singh, R (rsingh)

11485 B, 11785 B

The self-paced Meaning in Language Lab is intended to follow-up on the 11-725 lecture course (Meaning in Language) by providing a chance for hands-on, in-depth, computational exploration of various semantics and pragmatics research topics. The course is self-paced and there will be no scheduled lecture times, however, students are welcome to set up meetings with the instructor as desired, and students who prefer to have a weekly or bi-monthly regularly scheduled meeting with the instructor are welcome to arrange for that. If there is sufficient interest, an informal reading group may be formed to supplement the lab work. Students will design their own project, which they will discuss with the instructor for approval. Students are encouraged to select a topic from semantics, pragmatics, or discourse analysis, such as entailment, evidentiality, implicature, information status, or rhetorical structure, and a topic from language technologies, such as sentiment analysis or summarization, and explore how the linguistic topic applies to some aspect of the chosen language technology. Students are encouraged to contrast symbolic, formal, and knowledge based approaches with empirical approaches. Each student will work independently. If multiple students work as a team on a particular topic, each should choose an approach that is different from the approaches used by the other students working on the same problem. Students will be responsible to set up a web page, blog, or wiki to post progress reports and other supporting documents, data, and analyses. The web space will be checked by the instructor periodically , and thus should be kept updated in order to reflect on-going progress. The web space will also serve as a shared project space in the case that students are working in a team for the project.

Rose, C (cp3a)

Neural networks have increasingly taken over various AI tasks, and currently produce the state of the art in many AI tasks ranging from computer vision and planning for self-driving cars to playing computer games. Basic knowledge of NNs, known currently in the popular literature as "deep learning", familiarity with various formalisms, and knowledge of tools, is now an essential requirement for any researcher or developer in most AI and NLP fields. This course is a broad introduction to the field of neural networks and their "deep" learning formalisms. The course traces some of the development of neural network theory and design through time, leading quickly to a discussion of various network formalisms, including simple feedforward, convolutional, recurrent, and probabilistic formalisms, the rationale behind their development, and challenges behind learning such networks and various proposed solutions. We subsequently cover various extensions and models that enable their application to various tasks such as computer vision, speech recognition, machine translation and playing games. Instruction Unlike prior editions of 11-785, the instruction will primarily be through instructor lectures, and the occasional guest lecture. Evaluation Students will be evaluated based on weekly continuous-evaluation tests, and their performance in assignments and a final course project. There will be six hands-on assignments, requiring both low-level coding and toolkit-based implementation of neural networks, covering basic MLP, convolutional and recurrent formalisms, as well as one or more advanced tasks, in addition to the final project.

Ramakrishnan, B (bhikshar); Singh, R (rsingh)

Neural networks have increasingly taken over various AI tasks, and currently produce the state of the art in many AI tasks ranging from computer vision and planning for self-driving cars to playing computer games. Basic knowledge of NNs, known currently in the popular literature as "deep learning", familiarity with various formalisms, and knowledge of tools, is now an essential requirement for any researcher or developer in most AI and NLP fields. This course is a broad introduction to the field of neural networks and their "deep" learning formalisms. The course traces some of the development of neural network theory and design through time, leading quickly to a discussion of various network formalisms, including simple feedforward, convolutional, recurrent, and probabilistic formalisms, the rationale behind their development, and challenges behind learning such networks and various proposed solutions. We subsequently cover various extensions and models that enable their application to various tasks such as computer vision, speech recognition, machine translation and playing games. Instruction Unlike prior editions of 11-785, the instruction will primarily be through instructor lectures, and the occasional guest lecture. Evaluation Students will be evaluated based on weekly continuous-evaluation tests, and their performance in assignments and a final course project. There will be six hands-on assignments, requiring both low-level coding and toolkit-based implementation of neural networks, covering basic MLP, convolutional and recurrent formalisms, as well as one or more advanced tasks, in addition to the final project.

Ramakrishnan, B (bhikshar); Singh, R (rsingh)

Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with language vision projects such as image and video captioning, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities. The course will present the fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. These include, but not limited to, multimodal auto-encoder, deep canonical correlation analysis, multi-kernel learning, attention models and multimodal recurrent neural networks. We will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for MMML and discuss the current and upcoming challenges. The course will discuss many of the recent applications of MMML including multimodal affect recognition, image and video captioning and cross-modal multimedia retrieval. This is a remote offering only available to certificate seeking students.

Bisk, Y (ybisk)

Communications skills are fundamental to professionals in all fields, from architecture to software engineering to zoology, because they enable the exchange of ideas and the completion of organizational goals. The ability to identify an audience, to develop clear, persuasive presentations and written documents, and to handle the complex interactions that occur in the workplace make the difference between those who participate in an organization and those who lead it. This is the second course in a two-semester sequence designed to help you build and refine your skills so that you can communicate as a leader in your professional work. Through a combination of in-class exercises, case studies, projects, presentations, and written assignments, you will assess your current skill level and build upon it.

Frollini, D (df2x)

This course is for students enrolled in the Privacy by Design Practicum or by permission of the instructor. Students will discuss privacy by design challenges and solutions.

Habib, H (htq)

Students in this course will work in small teams on a large semester-long Privacy By Design project for a project sponsor. Students will be expected to deliver a final report and project presentation at the end of the semester. This course is for students in the MSIT-Privacy Engineering program or by permission of the instructor.

variable units

Habib, H (htq)

Berardone, J (jberar)

Berardone, J (jberar)

Berardone, J (jberar)

Berardone, J (jberar)

Berardone, J (jberar)

Berardone, J (jberar)

Prompt Engineering examines the science behind language model prompting and the strategies by which to design prompt-based systems. Students in this course will learn a brief history of large language models (LLM) as well as contemporary approaches to LLM design and development, such as instruction-tuning, alignment, and calibration. Under alignment, students will study hallucinations, bias, sycophancy, immorality and deception by LLMs. Students will learn about contemporary prompt engineering strategies and techniques, including chain-of-thought, retrieval augmented generation (RAG), tool usage, various ways to self-prompt for response verification and consistency and agent-based prompting, including the use of personas, and benefits of agent-based debate and collaboration. The course covers standard prompt engineering benchmarks and evaluation metrics to evaluate the efficacy of prompt designs. Finally, students will practice using cloud-based language models to complete coursework. Various options exist, including OpenAI's GPT, Google's Gemini, multiple models, including Anthropic's Claude, available on Amazon Bedrock. Class tutorials exist to guide students on how to setup and use one of these services.

Breaux, T (tdbreaux)

Prompt Engineering examines the science behind language model prompting and the strategies by which to design prompt-based systems. Students in this course will learn a brief history of large language models (LLM) as well as contemporary approaches to LLM design and development, such as instruction-tuning, alignment, and calibration. Under alignment, students will study hallucinations, bias, sycophancy, immorality and deception by LLMs. Students will learn about contemporary prompt engineering strategies and techniques, including chain-of-thought, retrieval augmented generation (RAG), tool usage, various ways to self-prompt for response verification and consistency and agent-based prompting, including the use of personas, and benefits of agent-based debate and collaboration. The course covers standard prompt engineering benchmarks and evaluation metrics to evaluate the efficacy of prompt designs. Finally, students will practice using cloud-based language models to complete coursework. Various options exist, including OpenAI's GPT, Google's Gemini, multiple models, including Anthropic's Claude, available on Amazon Bedrock. Class tutorials exist to guide students on how to setup and use one of these services.

Breaux, T (tdbreaux)

Projects are temporary organizations set up to achieve a one time objective in an agreed time frame. They are characterized by requiring the execution of interrelated, normally non repeating activities, by multidisciplinary groups. Because of its temporary nature and the interrelatedness of its activities, projects require prescriptive planning, budgeting, staffing and risk management. This course will introduce student to fundamental project management techniques and tools such as activity planning, milestone planning, estimation, work breakdown structures, critical paths. The course will also look at hybrid methods such as Milestone Driven Agile Execution and Disciplined Agile Delivery.

Chick, T (tchick); Kumar, A (aseethar)

Successful design of complex software systems requires the ability to describe, evaluate, and create systems at an architectural level of abstraction. This course introduces architectural design of complex software systems. The course considers commonly-used software system structures, techniques for designing and implementing these structures, models and formal notations for characterizing and reasoning about architectures, tools for generating specific instances of an architecture, and case studies of actual system architectures. It teaches the skills and background students need to evaluate the architectures of existing systems and to design new systems in principled ways using well-founded architectural paradigms. After completing this course, students will be able to: 1. describe an architecture accurately 2. recognize major architectural styles in existing software systems 3. generate architectural alternatives for a problem and choose among them 4. construct a medium-sized software system that satisfies an architectural specification 5. use existing definitions and development tools to expedite such tasks 6. understand the formal definition of a number of architectures and be able to reason about the properties of those architectures 7. use domain knowledge to specialize an architecture for a particular family of applications. If AWS Credits are required to complete coursework, those credits must be purchased by the student.

Ashok, S (swarnala); Pavetti, S (spavetti)

Successful design of complex software systems requires the ability to describe, evaluate, and create systems at an architectural level of abstraction. This course introduces architectural design of complex software systems. The course considers commonly-used software system structures, techniques for designing and implementing these structures, models and formal notations for characterizing and reasoning about architectures, tools for generating specific instances of an architecture, and case studies of actual system architectures. It teaches the skills and background students need to evaluate the architectures of existing systems and to design new systems in principled ways using well-founded architectural paradigms. After completing this course, students will be able to: 1. describe an architecture accurately 2. recognize major architectural styles in existing software systems 3. generate architectural alternatives for a problem and choose among them 4. construct a medium-sized software system that satisfies an architectural specification 5. use existing definitions and development tools to expedite such tasks 6. understand the formal definition of a number of architectures and be able to reason about the properties of those architectures 7. use domain knowledge to specialize an architecture for a particular family of applications. If AWS Credits are required to complete coursework, those credits must be purchased by the student.

Ashok, S (swarnala); Pavetti, S (spavetti)

Managing software quality is a critical part of all software projects. Software engineers must consider quality during every phase of a project from inception to delivery and beyond. This class will introduce students to the managerial challenges of developing high quality software systems. The key learning objectives of this course include: 1. Define a quality management process in the context of a software project. 2. Understand the costs associated with achieving quality goals and not achieving them 3. Understand the tradeoffs required to implement quality assurance techniques. 4. Gain experience using collected quality metrics to inform project-level decisions.

Timperley, C (ctimperl); Gennari, J (jgennari)

The course takes a software engineering perspective on building software systems with a significant machine learning or AI component. It discusses how to take an idea and a model developed by a data scientist (e.g., scripts and Jupyter notebook) and deploy it as part of scalable and maintainable system (e.g., mobile apps, web applications, IoT devices). Rather than focusing on modeling and learning itself, this course assumes a working relationship with a data scientist and focuses on issues of design, implementation, operation, and assurance and how those interact with the data scientist's modeling. This course is aimed at software engineers who want to understand the specific challenges of working with AI components and at data scientists who want to understand the challenges of getting a prototype model into production; it facilitates communication and collaboration between both roles.

Le Goues, C (clegoues); Kaestner, C (ckaestne)

Internet services companies such as Google, Amazon, Netflix, and Meta, have pioneered systems that have achieved unprecedented scale while still providing high level availability and a high cost-performance. These systems differ from mainstream high performance systems in fundamental ways. They are data intensive rather than compute intensive. They are also cloud-native distributed systems that are often deployed as microservices and require integration with external systems via networked-based APIs. They need to inherently support scalability, typically having high throughput, security, reliability and availability demands as well. Designing and building these systems require a specialized set of skills. This course will cover design principles and strategies needed to design and implement data intensive scalable distributed systems. In this domain engineers not only need to know how to architect systems that are inherently scalable, but to do so in a way that also supports high availability, reliability, and performance. Given the nature of these systems, basic distributed systems concepts such as time, transparency, data consistency and synchronization are also important. These systems largely operate around the clock, have components running on different nodes and platforms, so the course also emphasizes deployability and operational concerns. The course includes a hands-on project where students get to create and discuss design alternatives, and implement the solutions in a public cloud environment. The basic concepts will be given during the lectures and applied in the project. The students will gain exposure to the core concepts needed to design and build such systems as well as current technologies in this space. Class size will be limited.

Merson, P (pmerson)

variable units

Instructor TBA

In this seminar course students will discuss recent papers and current public policy issues related to privacy. Privacy professionals from industry, government, and non-profits will deliver several guest lectures each semester.

Sadeh, N (ns1i); Habib, H (htq)

variable units

Sadeh, N (ns1i)

There is growing recognition that technology alone will not provide all of the solutions to security and privacy problems. Human factors play an essential role in these areas, and it is important for security and privacy experts to have an understanding of how people will interact with the systems they develop. This course is designed to introduce students to a variety of usability and user-interface problems related to privacy and security and to give them experience in understanding and designing studies aimed at helping to evaluate usability issues in security and privacy systems. The course is suitable both for students interested in privacy and security who would like to learn more about usability, as well as for students interested in usability who would like to learn more about security and privacy. All students will work in small teams on a group project throughout the semester. The course is open to all students who have at least some technical background (e.g. an undergraduate computer programming course). The 12-unit course numbers (17-734, 5-836, 19-734) are for PhD students and masters students (but open to undergrads). Students enrolled in these course numbers will be required to read and comment on a research paper each week in addition to the other assignments. The 9-unit course numbers (8-534, 5-436, 19-534) are for undergraduates. Most seats open to students in any department are available in 17-334 and 17-734. Remote sections are available for students in Qatar and Africa, Privacy Engineering part-time students, and other remote students with permission of the instructor.

Cranor, L (lorrie); Agarwal, Y (yuvraja)

With the advent of privacy legislation, such as the European Union's General Data Protection Regulation (GDPR) and California's Consumer Privacy Act (CCPA), as well as growing consumer privacy concern, there is expanded interest in privacy engineering tools that identify and mitigate privacy harms arising from digital technology use. In this course we will first explore existing frameworks for identifying and classifying privacy threats during software system development. We will then discuss implementations of existing privacy-enhancing technologies and how they can be used in real-world applications. This course is lecture-based and includes a semester-long project in which students will develop a solution to a privacy problem utilizing existing privacy-enhancing technologies.

Habib, H (htq)

This course is about developing and using measurement systems to support managerial decision-making and performance evaluation. Because we focus on firms¿ internal decisions, we are not constrained by the rules and regulations that surround financial accounting. Rather, we work towards developing a conceptual framework that emphasizes the costs and benefits associated with using a particular measurement system. Our goal is to understand how accounting information fits into the dynamics of managing a complex entity.

Sudbury, A (asudbury)

This course is about developing and using measurement systems to support managerial decision-making and performance evaluation. Because we focus on firms¿ internal decisions, we are not constrained by the rules and regulations that surround financial accounting. Rather, we work towards developing a conceptual framework that emphasizes the costs and benefits associated with using a particular measurement system. Our goal is to understand how accounting information fits into the dynamics of managing a complex entity.

Sudbury, A (asudbury)

This course is about developing and using measurement systems to support managerial decision-making and performance evaluation. Because we focus on firms¿ internal decisions, we are not constrained by the rules and regulations that surround financial accounting. Rather, we work towards developing a conceptual framework that emphasizes the costs and benefits associated with using a particular measurement system. Our goal is to understand how accounting information fits into the dynamics of managing a complex entity.

Sudbury, A (asudbury)

This course covers fundamental optimization tools for quantitative analysis in the management sciences. The central topics of study are linear integer and nonlinear programming. Special emphasis is placed on linear programming particularly on modeling business applications and on sensitivity analysis. The course follows a practical spreadsheet-based approach to provide hands-on experience with software such as Excel Solver.

Kilinc-Karzan, F (fkilinc)

This course covers fundamental optimization tools for quantitative analysis in the management sciences. The central topics of study are linear integer and nonlinear programming. Special emphasis is placed on linear programming particularly on modeling business applications and on sensitivity analysis. The course follows a practical spreadsheet-based approach to provide hands-on experience with software such as Excel Solver.

Kilinc-Karzan, F (fkilinc)

This course covers fundamental optimization tools for quantitative analysis in the management sciences. The central topics of study are linear integer and nonlinear programming. Special emphasis is placed on linear programming particularly on modeling business applications and on sensitivity analysis. The course follows a practical spreadsheet-based approach to provide hands-on experience with software such as Excel Solver.

Kilinc-Karzan, F (fkilinc)

Description: The object of this course is to provide a simple but rigorous framework for understanding modern macroeconomic issues, debates, crises and solutions. We will develop simple models of the economy that can be used to better understand the behavior of the economy as a whole and the sources of the various controversies concerning macroeconomic policy. The class notes and topics are updated every mini and throughout the course in an effort to incorporate both new research in macroeconomics and topical issues in the US economy and worldwide.

Saeedi, M (msaeedi)

Description: The object of this course is to provide a simple but rigorous framework for understanding modern macroeconomic issues, debates, crises and solutions. We will develop simple models of the economy that can be used to better understand the behavior of the economy as a whole and the sources of the various controversies concerning macroeconomic policy. The class notes and topics are updated every mini and throughout the course in an effort to incorporate both new research in macroeconomics and topical issues in the US economy and worldwide.

Saeedi, M (msaeedi)

Description: The object of this course is to provide a simple but rigorous framework for understanding modern macroeconomic issues, debates, crises and solutions. We will develop simple models of the economy that can be used to better understand the behavior of the economy as a whole and the sources of the various controversies concerning macroeconomic policy. The class notes and topics are updated every mini and throughout the course in an effort to incorporate both new research in macroeconomics and topical issues in the US economy and worldwide.

Saeedi, M (msaeedi)

This course focuses on strategy as the search for value among opportunities that are entrepreneurial, dynamic, and evolutionary. Competitive strategy in the new economy reflects a mix of traditional strategies, designed to sustain advantage through physical assets and traditional economies of scale, as well as newer strategies, designed to gain advantage through information assets and network effects. The approach used in this course to tie old and new approaches together is Sustainability Analysis, a method that formulates business strategies according to the profit half-life of a company?s products and services. Here the goal of strategy is not only to sustain advantage, but to maximize value, to manage the ebb and flow of economic progress with the continual goal of recapitalizing assets.

Lee, S (sunkee)

This course focuses on strategy as the search for value among opportunities that are entrepreneurial, dynamic, and evolutionary. Competitive strategy in the new economy reflects a mix of traditional strategies, designed to sustain advantage through physical assets and traditional economies of scale, as well as newer strategies, designed to gain advantage through information assets and network effects. The approach used in this course to tie old and new approaches together is Sustainability Analysis, a method that formulates business strategies according to the profit half-life of a company?s products and services. Here the goal of strategy is not only to sustain advantage, but to maximize value, to manage the ebb and flow of economic progress with the continual goal of recapitalizing assets.

Lee, S (sunkee)

This course focuses on strategy as the search for value among opportunities that are entrepreneurial, dynamic, and evolutionary. Competitive strategy in the new economy reflects a mix of traditional strategies, designed to sustain advantage through physical assets and traditional economies of scale, as well as newer strategies, designed to gain advantage through information assets and network effects. The approach used in this course to tie old and new approaches together is Sustainability Analysis, a method that formulates business strategies according to the profit half-life of a company?s products and services. Here the goal of strategy is not only to sustain advantage, but to maximize value, to manage the ebb and flow of economic progress with the continual goal of recapitalizing assets.

Lee, S (sunkee)

STUDENTS ARE NOT PERMITTED TO TAKE THIS COURSE FOR PASS/FAIL 1. Course objectives: This course is about fundamental analysis using financial statements. We develop and apply tools to help us understand firm activities that generate shareholder value. We also study earnings management, earnings quality, and the financial reporting regulatory environment in order to develop a more refined understanding of financial statements and how to make adjustments to them to improve your analysis. 2. Features of the approach: -Builds from first principles -Compares different approaches -Stresses fundamental analysis and, in particular, the distinction between operating and financing activities to isolate the value drivers -Exploits accounting as a system for measuring value added Here is a list of topics we address: -Which firm activities generate value? -How do we evaluate earnings quality (persistence)? -How do we pull apart and reformulate financial statements to get at the relevant information? -How should we do ratio analysis to reflect the underlying economic activities of the firm? 3. Course organization: The course uses a combination of lectures, cases, and a take-home final exam. Your participation in case discussions is a key component of the course

Sudbury, A (asudbury)

Commercialization and Innovation, Strategy (45-807) focuses on innovation (transformational or disruptive innovations and by sustained innovations) and on the development of open innovation business models and market strategies required to introduce these innovations into the market, grow thru market share capture, and to establish dominant market positions. Students will gain a perspective of various current theories and models of innovation, how innovations are brought to the market and positioned for successful launch and subsequent growth. Students study and discuss both successful and unsuccessful attempts to bring innovations to the market via a series of lectures, readings, and case discussions. The first mini course focuses on the upfront strategic market thinking that must be the basis of a proactive and potent business plan to introduce innovations to the marketplace. It is the result of intense understanding of the SET factors (social, economic, and technology) and industry dynamics into which the opportunity will be introduced. It is strategic because of rapid changes in the marketplace and the competitive-set which the opportunity must confront for execution in the emerging marketplace (emergent strategy or agile approach). Student teams are expected to take on a project determined by the team (with faculty approval) or framed by an outside organization (within or external to CMU). The goal of the project in Mini 1 is to understand the industry dynamics and competitive set, to identify a market based on use of an "agile needs-driven innovation" methodology (job + job executor + context defines the market), and to segment the market based on identification of "jobs to be done" by the job executors. These are the drivers for identification of a successful Minimum Viable Product (MVP), a market entry point, and development of a differentiating strategy and self-sustained growth strategy.

Markovitz, C (cmarkovi)

Finance II is the prerequisite for all finance electives. The course develops the concepts and tools needed to analyze publicly traded securities, and to apply the tools to real world situations problems such as optimal portfolio formation, cost of capital estimation, interest rate risk management, and basic derivatives valuation techniques.

Kuehn, L (lakuehn)

The objective of Investment Analysis is to introduce you to the tools used by investment professionals to manage assets and their risks. The course covers optimal asset allocation, its performance evaluation, and risk management. The goal is to apply basic tools in finance as such mean-variance portfolio optimization and gain a better understanding why and when these tools fail. As such we will talk about the measurement and implications of tail events, the impact of liquidity on prices and trading, the risk of default, and the implications of return predictability.

Foster, J (jimf)