Spring 2020: Deep Learning: Syllabus and Schedule

Time/Location: Mon/Wed 2:30-3:45pm in room MCS B33 / Zoom

Sections: CAS CS 591 S1 (will be CS 523)

Instructor: Kate Saenko, saenko@bu.edu

Teaching Assistant: Samarth Mishra, samarthm@bu.edu

Piazza: registered students can access via piazza.com/bu/spring2020/cs591s1spr20

Schedule*

*schedule is tentative and is subject to change.

Course Description

This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions and self-driving cars. We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like text generation and computer vision.

Course Prerequisites

This is a graduate course that is also open to undergraduates with sufficient background. All students should have the following skills:

• Solid math skills, including

• Linear algebra (minimum CAS CS 132 or MA 242 or equivalent)
• Multivariate Calculus (eg, CAS MA 225), including partial derivatives
• Probability & Statistics (eg, CAS CS 237 or MA 381 or 581 or equivalent)

• Comfort with coding in Python, NumPy
• Background in machine learning such as one of:

• CAS CS 542 Machine Learning, or
• ENG EC 503 Learning from Data, or equivalent.

If you lack any of the above prerequisites, you probably should not take the course, so please talk to the instructor. The first homework will test your knowledge of basic machine learning concepts, please use it as a self-assessment if you are not sure if you have enough background. Students must be able to do well on HW1 to do well in the course.

Textbook

The required textbook for the course is

• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. 

Other recommended supplemental textbooks on general machine learning:

• Duda, R.O., Hart, P.E., and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
• Theodoridis, S. and Koutroumbas, K. Pattern Recognition. Edition 4. Academic Press, 2008.
• Russell, S. and Norvig, N. Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence. 2003.
• Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press. 1995.
• Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning. Springer. 2001.
• Koller, D. and Friedman, N. Probabilistic Graphical Models. MIT Press. 2009.

Recommended online courses

• http://cs231n.stanford.edu/ CS231n: Convolutional Neural Networks for Visual Recognition
• http://web.stanford.edu/class/cs224n/ CS224n: Natural Language Processing with Deep Learning
• http://rll.berkeley.edu/deeprlcourse/ CS 294: Deep Reinforcement Learning
• http://distill.pub/ Very nice explanations of some DL concepts

Deliverables/Graded Work

There will be six homework assignments, each consisting of written and/or coding problems, and a final project. The project will be done in teams of 2 students and will have several deliverables including a proposal, progress updates, final report and a final in-class presentation. The course grade consists of the following (note, HW6 is now optional):

• Homeworks, HW1 plus best 3 of HW2-5         50%
• Project (including all components)                 40%
• Class participation                         10%

Software/Hardware

Programming assignments and projects will be developed in the Python programming language. We will also use the PyTorch deep learning library for some homeworks and for the project. Students are expected to use the Shared Computing Cluster (SCC) and/or their own machines to complete work that does not require a GPU. For the projects, we will provide GPU resources.

If you do not already have a CS account and would like one, see here: http://www.bu.edu/cs/resources/laboratories/undergraduate-lab/

Checking GPU usage on SCC

Projects

The projects are open-ended and should be done in teams of two. The deliverables include:

• proposal (2 pages)
• progress update
• final presentation, in class
• code on Github (or proofs) and
• final report (6-8 pages)

The page length above is based on a 2-person team, and should be scaled accordingly if for some reason your team is larger. The project should involve significant implementation or derivation effort (in terms of written lines of code or proofs if theoretical in nature). For example, downloading existing code and running it on an existing dataset without additional implementation effort is not adequate. On the other hand, re-implementing a research paper from scratch and testing the implementation to try to reproduce the results is an example of a good project.

Some useful info: sample projects, project proposal template, update template, final report template (same as update but replace preliminary results with final results and conclusions).

Late Policy

Late work will incur the following penalties

• Homework, project reports: 20% off per day up to 2 days
• Final project presentation: no late presentations accepted

Academic Honesty Policy

The instructors take academic honesty very seriously. Cheating, plagiarism and other misconduct may be subject to grading penalties up to failing the course. Students enrolled in the course are responsible for familiarizing themselves with the detailed BU policy, available here. In particular, plagiarism is defined as follows and applies to all written materials and software, including material found online. Collaboration on homework is allowed, but should be acknowledged and you should always come up with your own solution rather than copying (which is defined as plagiarism):

Plagiarism: Representing the work of another as one’s own. Plagiarism includes but is not limited to the following: copying the answers of another student on an examination, copying or restating the work or ideas of another person or persons in any oral or written work (printed or electronic) without citing the appropriate source, and collaborating with someone else in an academic endeavor without acknowledging his or her contribution. Plagiarism can consist of acts of commission-appropriating the words or ideas of another-or omission failing to acknowledge/document/credit the source or creator of words or ideas (see below for a detailed definition of plagiarism). It also includes colluding with someone else in an academic endeavor without acknowledging his or her contribution, using audio or video footage that comes from another source (including work done by another student) without permission and acknowledgement of that source.

Religious Observance

Students are permitted to be absent from class, including classes involving examinations, labs, excursions, and other special events, for purposes of religious observance.  In-class, take-home and lab assignments, and other work shall be made up in consultation with the student’s instructors. More details on BU’s religious observance policy are available here.