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1 | Computational Biology PhD Program, Required courses (see website for more information): -- CMPBIO 293 (Doctoral Student Seminar), Fall & Spring -- Stat 201A/B (Can be taken together or in consecutive Falls. Students can test out. If students do not test in, they must take STAT 134 or PH 142 first) -- CS61A (Fall Semester). -- Three additional courses from the list below (non-listed courses ok, with pre-approval) --- 1 course each should be taken from clusters A and B --- At least 1 course should be at the graduate level --- CS176 (cluster B) is highly recommended, with exceptions based on background --- 1 course can be elective or from either cluster -- Exceptions to course policy at Head Grad Advisor's discretion | MCB = Molecular and Cell Biology IB = Integrative Biology CS = Computer Science BioE/Bio Eng = Bioengineering EE = Electrical Engineering Stat = Statistics PMB = Plant and Microbial Biology | ||||||||||||||||||||||||

2 | Cluster ID | Theme | Theme ID | Course | Course title | Topics relevant to CompBio students | Course decription | Last taught (or closest semester) | Points | URL | ||||||||||||||||

3 | Cluster A: Biological Science Courses | 1 | ||||||||||||||||||||||||

4 | 1 | Molecular Biology and Macromolecular Structure | 11 | |||||||||||||||||||||||

5 | 1 | 11 | MCB (MCELLBI) 110 | Molecular Biology: Macromolecular Synthesis and Cellular Function | DNA replication/repair and mutation; gene expression regulation; chromatin and epigenetics | Key concepts in genetic analysis, eukaryotic cell biology, and state-of-the-art approaches in genomic medicine. Lectures will highlight basic knowledge of cellular processes with the basis for human diseases, particularly cancer. Prerequisite courses will have introduced students to the concepts of cells, the central dogma of molecular biology, and gene regulation. Emphasis in this course will be on eukaryotic cell processes, including cellular organization, dynamics, and signaling. | Fall, Spring 2014-15 (offered both semesters) | 4 | Syllabus) | |||||||||||||||||

6 | 1 | 11 | MCB (MCELLBI) 200A-B (two courses) | Fundamentals of Molecular and Cell Biology | DNA replication/repair and mutation; inheritance and mutation; gene expression regulation; regulatory sequences and mutation; chromatin and epigenetics | Six hours of lecture per week. Prerequisites: 200A and 200B must be taken concurrently. Combined course required for all MCB first-year graduate students. The goal of this course is to provide graduate-level instruction on molecular and cellular biosciences from a highly-integrated systems perspective, rather than using a more classic, techniques-oriented format. A collection of approaches, and a focus on critical thinking and problem solving, will be used to show how fundamental, highly-significant biological problems are "cracked open." Reading will be assigned from a mix of classic and current peer-reviewed papers selected by the instructors. | Fall 2014 (fall only) | 4 | View Syllabus | |||||||||||||||||

7 | 1 | 11 | MCB (MCELLBI) 206 | Physical Biochemistry | Protein structure; protein evolution | Application of modern physical concepts and experimental methods to the analysis of the structure, function, and interaction of large molecules of biological interest | Spring 2015 | 3 | View Syllabus | meet the Advanced Topics requirement MCB | ||||||||||||||||

8 | 1 | 11 | MCB (MCELLBI) 210 | Macromolecular Reactions and the Cell | DNA replication/repair and mutation; gene expression regulation; chromatin and epigenetics; biochemistry methodology | General course for first-year graduate students. Covers our current understanding of, methodological approaches for analyzing, and recent advances in the function of cellular macromolecules and macromolecular complexes in DNA replication, recombination, transposition and repair, gene expression and its regulation, mRNA splicing, genome organization, non-coding RNAs, signal transduction, protein synthesis, folding and degradation, growth control, and other life processes. | Spring 2015 (spring only) | 4 | View Syllabus | meet the Advanced Topics requirement and highly recommended MCB | ||||||||||||||||

9 | 1 | 11 | PMB (PLANTBI) C244 / Bio Eng C244 | Introduction to Protein Informatics | Protein evolution; structural bioinformatics | This course will introduce students to the fundamentals of molecular biology, and to the bioinformatics tools and databases used for the prediction of protein function and structure. It is designed to impart both a theoretical understanding of popular computational methods, as well as some experience with protein sequence analysis methods applied to real data. This class includes no programming, and no programming background required. Also listed as Plant and Microbial Biology C244. | Fall 2014 | 4 | ||||||||||||||||||

10 | 1 | Molecular Genetics and Genomics | 12 | |||||||||||||||||||||||

11 | 1 | 12 | MCB (MCELLBI) 140 | General Genetics | Inheritance and mutation; regulatory sequences and mutations | In-depth introduction to genetics, including mechanisms of inheritance; gene transmission and recombination; transposable DNA elements; gene structure, function, and regulation; and developmental genetics. | Fall, Spring 2014- 15 (generally only offered in the fall) | 4 | https://mcb.berkeley.edu/courses/syllabi/mcb140_fall2013.pdf | |||||||||||||||||

12 | 1 | 12 | PMB (PLANTBI) C148 / MCB (MCELLBI) C148 | Microbial Genomics and Genetics | Microbial genomics; comparative genomics; metagenomics | bacterial and archaeal genetics and comparative genomics. Genetics and genomic methods used to dissect metabolic and development processes in bacteria, archaea, and selected microbial eukaryotes. Genetic mechanisms integrated with genomic information to address integration and diversity of microbial processes. Introduction to the use of computational tools for a comparative analysis of microbial genomes and determining relationships among bacteria, archaea, and microbial eukaryotes. | Spring 2015 (spring only) | 4 | ||||||||||||||||||

13 | 1 | 12 | IB (INTEGBI) 164 | Human Genetics and Genomics | Population genetics; molecular evolution | This course will introduce students to basic principles of genetics, including transmissions genetics, gene regulation, pedigree analysis, genetic mapping, population genetics, and the principles of molecular evolution. The course will also introduce students to recent developments in genomics as applied to problems in human genetic diseases, human history, and the relationship between humans and their closest relatives. | Fall 2014 (fall only) | |||||||||||||||||||

14 | 1 | 12 | MCB (MCELLBI) 200A-B (two courses) | Fundamentals of Molecular and Cell Biology | DNA replication/repair and mutation; inheritance and mutation; gene expression regulation; regulatory sequences and mutation; chromatin and epigenetics | Six hours of lecture per week. Prerequisites: 200A and 200B must be taken concurrently. Combined course required for all MCB first-year graduate students. The goal of this course is to provide graduate-level instruction on molecular and cellular biosciences from a highly-integrated systems perspective, rather than using a more classic, techniques-oriented format. A collection of approaches, and a focus on critical thinking and problem solving, will be used to show how fundamental, highly-significant biological problems are "cracked open." Reading will be assigned from a mix of classic and current peer-reviewed papers selected by the instructors. | Fall 2014 (fall only) | 4 | View Syllabus | |||||||||||||||||

15 | 1 | 12 | MCB (MCELLBI) 240 | Advanced Genetic Analysis | Inheritance and mutation; regulatory sequences and mutations; genetics methodology | Principles and practice of classical and modern genetic analysis as applied to eukaryotic organisms, including yeast, nematodes, Drosophila, mice and humans; isolation and analysis of mutations; gene mapping; suppressor analysis; chromosome structure; control of gene expression; and developmental genetics. | Spring 2015 | View Syllabus | https://mcb.berkeley.edu/courses/mcb240/ | |||||||||||||||||

16 | 12 | CS 294 / COMPBIO 290 | Special topics course in computational molecular biology | Algorithms for functional genomics: chromatin profiling, network analysis, single-cell models. | Seminar course. Examine recent computational methods for modeling various mechanisms related to the regulation of gene expression, primarily based on high throughput sequencing data. | Spring 2015 | 3 | |||||||||||||||||||

17 | 1 | 13 | Stat C245E | Statistical Genomics I and II. | Population genetics; phylogenetics; high-throughput sequencing | C245E provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. C245F focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Neither course is a pre-requisite for the other. | Spring 2013 | |||||||||||||||||||

18 | 1 | 13 | Stat C245EF | Statistical Genomics I and II. | Population genetics; phylogenetics; high-throughput sequencing | C245E provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. C245F focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Neither course is a pre-requisite for the other. | Spring 2015 (spring only) | |||||||||||||||||||

19 | 12 | MCB (MCELLBI) C243 | *Seq: Methods and Applications | High-throughput sequencing | A graduate seminar class in which a group of students will closely examine recent computational methods in high-throughput sequencing followed by directly examining interesting biological applications thereof. | Spring 2014 | 3 | View Syllabus | ||||||||||||||||||

20 | PB HLTH 256A | Human Genome, Environment and Health | ||||||||||||||||||||||||

21 | PB HLTH 256B (PH256A must be taken concurrently) | Genetic and Genomic Analysis | ||||||||||||||||||||||||

22 | 1 | Population Genetics and Genomics | 13 | |||||||||||||||||||||||

23 | 1 | 13 | IB (INTEGBI) 161 | Population and Evolutionary Genetics | Population genetics; molecular evolution | study population genetic theory and use it to illuminate a number of different topics, including the existence of sex, altruism and cooperation, genome evolution speciation, and human genetic variation and evolution. | Spring 2015 (spring only) | 4 | ||||||||||||||||||

24 | 1 | 13 | IB (INTEGBI) 163/ MCB (MCELLBI)142 | Molecular and Genomic Evolution | Population genetics; population genomics; inheritance and mutation; molecular evolution | introduce undergraduates to the study of evolution using molecular and genomic methods. Topics included will be rates of evolution, evolution of sex chromosomes, insertions and deletions of DNA sequences, evolution of regulatory genetic elements, methods of phylogenetic inference, gene duplication, multigene families, transposons, genome organization, gene transfer, and DNA polymorphism within species. | Spring 2012 (spring only) | 4 | http://mcb.berkeley.edu/courses/mcb142/ | |||||||||||||||||

25 | 1 | 13 | IB (INTEGBI) 164 | Human Genetics and Genomics | Population genetics; molecular evolution | This course will introduce students to basic principles of genetics, including transmissions genetics, gene regulation, pedigree analysis, genetic mapping, population genetics, and the principles of molecular evolution. The course will also introduce students to recent developments in genomics as applied to problems in human genetic diseases, human history, and the relationship between humans and their closest relatives. | Fall 2014 (fall only) | 4 | ||||||||||||||||||

26 | 1 | 13 | IB (INTEGBI) 206 | Statistical Phylogenetics | Phylogenetics | Evolutionary models and methods for estimating phylogenies (which are trees representing how organisms are related to one another). Topics include continuous-time Markov chains as applied in phylogenetics; maximum likelihood estimation; Bayesian estimation; the combinatorics of evolutionary trees; Markov chain Monte Carlo; distance and parsimony methods for estimating trees; optimization strategies for finding best trees. | Fall 2011 (fall only) | 3 | ||||||||||||||||||

27 | 1 | 13 | Stat C245E | Statistical Genomics I and II. | Population genetics; phylogenetics; high-throughput sequencing | C245E provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. C245F focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Neither course is a pre-requisite for the other. | Spring 2013 | |||||||||||||||||||

28 | 1 | 13 | Stat C245EF | Statistical Genomics I and II. | Population genetics; phylogenetics; high-throughput sequencing | Spring 2015 (spring only) | ||||||||||||||||||||

29 | 1 | 13 | Bio Eng 241 | Probabilistic Modeling in Computational Biology | Phylogenetics; phylogenomics | the reconstruction of ancient genes and genomes by reverse Bayesian inference under various stochastic models of molecular evolution. | Spring 2014 (spring only) | 4 | https://schedulebuilder.berkeley.edu/explore/courses/SP/2012/5258 | |||||||||||||||||

30 | Cluster B: Computational | 2 | ||||||||||||||||||||||||

31 | 2 | Algorithms and software | 21 | |||||||||||||||||||||||

32 | 2 | 21 | CS 170 | Introduction to CS Theory | Basics of algorithms, Graph algoriothms, complexity | Basics of algorithms, Graph algoriothms, complexity | Fall, Spring 2014-15 (offered both semesters) | 4 | ||||||||||||||||||

33 | 2 | 21 | CS 270 | Combinatorial Algorithms and Data Structures | Linear programming; semi-definite programming | Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems | Spring 2015 | 4 | https://www.cs.berkeley.edu/~satishr/cs270/sp13/ | his course will focus on some of the most important modern algorithmic problems, such as clustering, and a set of beautiful techniques that have been invented to tackle them. The techniques include the use of geometry, convexity and duality, the formulation of computational tasks in terms of two person games and algorithms as two dueling subroutines. We will also explore the use of randomness in MCMC type algorithms and the use of concentration bounds in creating small core sets or sketches of input data, which can be used to quickly get a reasonable solution. We will also explore the use of these new techniques to speed up classical combinatorial optimization problems such as max-flow. | ||||||||||||||||

34 | 2 | 21 | CS 176 | Algorithms in Computational Biology | Sequence alignment, network alignment, phylogenetics, Markov processes. | Algorithms and probabilistic models that arise in various computational biology applications: suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, genome rearrangements, hidden Markov models | Fall 2014 (fall only) | 4 | ||||||||||||||||||

35 | 2 | 21 | Bio Eng 231 / 131 | Introduction to Computational Molecular and Cellular Biology | Sequence alignment, phylogenetics RNA structure prediction | computational approaches and techniques to gene structure and genome annotation, sequence alignment using dynamic programming, protein domain analysis, RNA folding and structure prediction, RNA sequence design for synthetic biology, genetic and biochemical pathways and networks, UNIX and scripting languages, basic probability and information theory. Various "case studies" in these areas are reviewed and web-based computational biology tools will be used by students and programming projects will be given | Fall 2013 (fall only) | 4 | ||||||||||||||||||

36 | 2 | 21 | CS 169 | Software Engineering | Handling large software projects; cloud computing | designing, developing, and modifying large software systems. Object-oriented and agile design techniques. Design patterns and modeling languages. Specification and documentation. Verification, static analysis, testing, version control, and debugging. Cost and quality metrics and estimation. Project team organization and management. | Fall, Spring 2014-15 (offered both semesters) | 4 | https://sites.google.com/site/ucbsaas/ | |||||||||||||||||

37 | 2 | 21 | CS 267 | Applications of Parallel Computers | dWorking with shared memory, multiple cpus | program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets | Spring 2015 (spring only) | ? | http://www.cs.berkeley.edu/~demmel/cs267_Spr14/ | |||||||||||||||||

38 | 2 | 21 | CS 286B | Implementation of database systems | database management | advanced database systems research from the 1970s to the present. We will cover a spectrum of topics, including storage and indexing, transaction processing, distributed databases, query optimization, large-scale data processing, streaming, approximate query processing, and advanced analytics | Fall 2014 | 4 | http://www.cs286.net/home/syllabus | |||||||||||||||||

39 | 2 | Statistics and Probability | 22 | |||||||||||||||||||||||

40 | 2 | 22 | Stat 134/EE 126 | Probability and Random Processes | Basic probability,distributions, random variables, markov chains, Poisson processes, conditional probabilities | provides an introduction to the basics of probability and random processes. This material is central to many fields in electrical engineering and computer science, including statistical signal processing, communications, control theory, and networking. It builds on the foundation of EE 20, and provides necessary background for higher-level courses, work and research. | Fall, Spring & Summer 2014-15 (offered every term) | 3 | http://www.stat.berkeley.edu/~aldous/134/ ; https://inst.eecs.berkeley.edu/~ee126/fa07/ | http://www-inst.eecs.berkeley.edu/~ee126/fa13/ | ||||||||||||||||

41 | 2 | 22 | Stat 135 | Concepts of Statistics | Max Likelihood, ANOVA | A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering. | Fall, Spring & Summer 2014-15 (offered every term) | 4 | http://www.stat.berkeley.edu/~nolan/stat135/Spr03/index.html | |||||||||||||||||

42 | 2 | 22 | Stat 150 | Stochastic Processes | Stochastic Processes | Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes | Fall, Spring 2014-15 (generally offered only in the spring) | 3 | http://www.stat.berkeley.edu/~aldous/150/ | |||||||||||||||||

43 | 2 | 22 | CS 174 | Combinatorics and Discrete Probability | Basics of algorithms, Graph theory, complexity | Permutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychevâ€™s inequality, Chernov bounds. Birthday paradox, coupon collectorâ€™s problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence bounds | Spring 2015 | 4 | http://www.cs.berkeley.edu/~jordan/courses/174-spring15/ | Combinatorics and Discrete Probability | ||||||||||||||||

44 | 2 | 22 | Stat 201A/B (required course) | Advanced intro to probability | Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables. Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. | Fall 2014 (fall only) | 4 | http://statistics.berkeley.edu/courses/fall-2013/fall-2013-stat-201a-001-lec | ||||||||||||||||||

45 | 2 | 22 | Stat 210A | Theoretical Statistics | introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation. | Fall 2014 (fall only) | 4 | http://www.cs.berkeley.edu/~jordan/courses/210A-fall14/ | ||||||||||||||||||

46 | 2 | 22 | Stat 215 A | Statistical Models: Theory and Application | Exploratory data analysis; Regression; GLM | Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso). | Fall 2014 (fall only) | 4 | ||||||||||||||||||

47 | 2 | 22 | Stat 215 B | Statistical Models: Theory and Application | Exploratory data analysis; Regression; GLM | Applied statistics with a focus on critical thinking, reasoning skills, and techniques. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso). | Spring 2014 (spring only) | 4 | ||||||||||||||||||

48 | 2 | 22 | Stat C205A/ Math 218A-B | Probability Theory | Designed for students whose ultimate research will involve rigorous proofs in mathematical probability. | This is the first half of a year course in mathematical probability at the measure-theoretic level. The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion. [very relevant to pop genetics]] | Fall 2014 (fall only) | 4 | http://www.stat.berkeley.edu/~aldous/205A/ | |||||||||||||||||

49 | 2 | 22 | Stat C205B/ Math 218A-B | Probability Theory | Designed for students whose ultimate research will involve rigorous proofs in mathematical probability. | This is the first half of a year course in mathematical probability at the measure-theoretic level. The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion. [very relevant to pop genetics]] | Spring 2014 (spring only) | 4 | http://www.stat.berkeley.edu/~aldous/205A/ | |||||||||||||||||

50 | 2 | 22 | Stat 204 | Probability for Applications | In contrast to STAT 205 (which emphasizes rigorous proof techniques) this course will emphasize describing what's known and how to do calculations in a broader range of probability models. Students are encouraged to learn by doing exercises. | Spring 2015 (not offered next academic year) | https://www.stat.berkeley.edu/~aldous/204/index.html | |||||||||||||||||||

51 | 2 | Machine Learning and optimization | 23 | |||||||||||||||||||||||

52 | 2 | 23 | CS 189/ CS 289A | Introduction to machine learning | clustering, dimensionality redeuction, classification, regression | Basic machine learning (clustering, dimensionality redeuction, classification, regression) | Spring 2015 (spring only) | 4 | http://inst.eecs.berkeley.edu/~cs189/sp15/ | |||||||||||||||||

53 | 2 | 23 | CS 281A/ Stat 241A | Statistical Learning Theory | Probabilistic graphical models | probabilistic and computational methods for the statistical modeling of complex, multivariate data. The emphasis will be on the unifying framework provided by graphical models, a formalism that merges aspects of graph theory and probability theory | Fall 2014 | 3 | http://www.cs.berkeley.edu/~jordan/courses/281A-spring14/ | |||||||||||||||||

54 | 2 | 23 | CS 281B / Stat 241B | Statistical Learning Theory | Prediction methods | introduction to the theoretical analysis of prediction methods, focusing on statistical and computational aspects. It will cover approaches such as kernel methods and boosting algorithms, and probabilistic and game theoretic formulations of prediction problems, and it will focus on tools for the theoretical analysis of the performance of learning algorithms and the inherent difficulty of learning problems. | Spring 2014 (spring only) | 3 | http://www.stat.berkeley.edu/~bartlett/courses/2014spring-cs281bstat241b/ | |||||||||||||||||

55 | 2 | 23 | EE 127/ 227AT | Optimization Models and Applications | Convex optimization and practial linear algebra (decomposition, linear equations) | ntroduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. The course covers two main topics: practical linear algebra and convex optimization | Spring 2015 | 4 | http://www.eecs.berkeley.edu/~elghaoui/Teaching/EE127/ScheduleNew.pdf | |||||||||||||||||

56 | 2 | 23 | EE 227B | Convex optimization | Convex optimization: duality, roustness | Convex optimization: convexity, conic optimization, duality. Selected topics: robustness, stochastic programming, applications | Fall 2015 | 4 | http://www.eecs.berkeley.edu/~elghaoui/Teaching/EE227BT/ | |||||||||||||||||

57 | 2 | Mathematical modeling and scientific computing | 24 | |||||||||||||||||||||||

58 | 2 | 24 | EE 219A | Numerical Simulation and Modeling | Numerical Simulation and Modeling | Fundamental concepts and algorithms in numerical simulation, including nonlinear and linear algebraic system solution, numerical algorithms for ODEs and DAEs, frequency-domain solution of linear(ized) systems and algorithms for simulating the effects of noise and parametric variability. | Fall 2014 (fall only) | 3 | http://potol.eecs.berkeley.edu/classWiki/tiki-index.php?page=EECS219A-Fall-2014 | |||||||||||||||||

59 | 2 | 24 | Math 128A | Numerical Analysis | Modeling and solving ODE systems | Basic concepts and methods in numerical analysis: Solution of equations in one variable; Polynomial interpolation and approximation; Numerical differentiation and integration; Initial-value problems for ordinary differential equations; Direct methods for solving linear systems. | Fall, Spring & Summer 2014-15 (offered every term) | 4 | http://persson.berkeley.edu/128A/ | |||||||||||||||||

60 | 2 | 24 | Math 228A | Numerical Solutions of Differential Equations | Modeling and solving DE systems | numerical solutions and theoretical treatment of differential equations and integral equations | Fall 2014 (fall only) | 4 | https://math.berkeley.edu/~mgu/MA228A/index.html | |||||||||||||||||

61 | 2 | 24 | EE 221A | Linear system theory | Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test. | introduction to the modern state space theory of linear systems for students of circuits, communications, controls and signal processing. In some sense it is a second course in linear systems, since it builds on an understanding that students have seen linear systems in use in at least some context before. The course is on the one hand quite classical and develops some rather well developed material, but on the other hand is quite modern and topical in that it provides a sense of the new vistas in embedded systems, computer vision, hybrid systems, distributed control, game theory and other current areas of strong research activity. | Fall 2014 | 4 | https://inst.eecs.berkeley.edu/~ee221a/fa14/ | |||||||||||||||||

62 | 2 | 24 | EE 222 | Nonlinear Systems--Analysis, Stability and Control | Introduction to nonlinear phenomena: multiple equilibria, limit cycles, bifurcations, complex dynamical behavior. Planar dynamical systems, analysis using phase plane techniques. Describing functions. Input-output analysis and stability. Lyapunov stability theory. The Lure problem, Circle and Popov criterion. Feedback linearization, sliding mode control. The course will be punctuated by a rich set of examples, ranging from violin strings to jet engines, from heart beats to artificial neurons, and from population growth to nonlinear flight control. | Spring 2015 (spring only) | 3 | http://www-inst.eecs.berkeley.edu/~ee222/sp15/ | ||||||||||||||||||

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67 | Other possible for algorithms: | |||||||||||||||||||||||||

68 | CS 271 | RANDOMNESS & COMPUTATION | http://www.cs.berkeley.edu/~sinclair/cs271/f11.html | Fall 2011 | ||||||||||||||||||||||

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71 | Stat 243 | Introduction to statistical computing | http://www.stat.berkeley.edu/~paciorek/teaching/teaching.html | Fall 2014 (fall only) | ||||||||||||||||||||||

72 | EE 226A | Random Processes in Systems | https://inst.eecs.berkeley.edu/~ee226a/fa09/ | Fall 2014 (fall only) | ||||||||||||||||||||||

73 | Background courses: | |||||||||||||||||||||||||

74 | Math 110 or EE 127 | Linear algebra | Spring 2015 (spring only) | |||||||||||||||||||||||

75 | CS 61A (required course) | Structure and Interpretation of Computer Programs (e.g., object oriented programming) | The CS 61 series is an introduction to computer science, with particular emphasis on software and machines from a programmer's point of view. CS 61A covered high-level approaches to problem-solving, providing you with a variety of ways to organize solutions to programming problems as compositions of functions, collections of objects, or sets of rules. In CS 61B, we move to a somewhat more detailed (and to some extent, more basic) level of programming. In 61A, the correctness of a program was our primary goal. In CS61B, we're concerned also with engineering. An engineer, it is said, is someone who can do for a dime what any fool can do for a dollar. Much of 61B will be concerned with the tradeoffs in time and memory for a variety of methods for structuring data. We'll also be concerned with the engineering knowledge and skills needed to build and maintain moderately large programs. | Fall, Spring 2014-15 (offered both semesters) | ||||||||||||||||||||||

76 | CS 61B | Data Structures | http://cs61b.ug/sp16/ | see description above | ||||||||||||||||||||||

77 | CS 70 | Discrete Mathematics and Probability Theory | http://www.eecs70.org/ | |||||||||||||||||||||||

78 | CS 164 | Programming Languages and Compilers | How comilers work | Introduction to the design of programming languages and the implementation of translators for them. In the process, we'll do some general exploration of programming language design and its implications for implementation, and look at a dialect of at least one particular language, which this semester is Python | Fall, Spring 2014-15 (offered both semesters) | |||||||||||||||||||||

79 | IB (INTEGBI) 160 | Evolution | An analysis of the patterns and processes of organic evolution. History and philosophy of evolutionary thought; the different lines of evidence and fields of inquiry that bear on the understanding of evolution. The major features and processes of evolution through geologic times; the generation of new forms and new lineages; extinction; population processes of selection, adaptation, and other forces; genetics, genomics, and the molecular basis of evolution; evolutionary developmental biology; sexual selection; behavorial evolution; applications of evolutionary biology to medical, agricultural, conservational, and anthropological research | Fall 2014 (fall only) | ||||||||||||||||||||||

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83 | http://bioeng.berkeley.edu/undergrad/program/bioetopics | |||||||||||||||||||||||||

84 | https://schedulebuilder.berkeley.edu/explore/courses/ | |||||||||||||||||||||||||

85 | http://general-catalog.berkeley.edu/catalog/gcc_list_crse_req?p_dept_name=Molecular+and+Cell+Biology&p_dept_cd=MCELLBI | |||||||||||||||||||||||||

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