Comp Bio Courses_2015.08
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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
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Cluster IDThemeTheme IDCourseCourse titleTopics relevant to CompBio studentsCourse decriptionLast taught (or closest semester)PointsURL
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Cluster A: Biological Science Courses1
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1Molecular Biology and Macromolecular Structure11
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111MCB (MCELLBI) 110Molecular Biology: Macromolecular Synthesis and Cellular FunctionDNA replication/repair and mutation; gene expression regulation; chromatin and epigeneticsKey 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)4Syllabus)
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111MCB (MCELLBI) 200A-B (two courses)Fundamentals of Molecular and Cell BiologyDNA replication/repair and mutation; inheritance and mutation; gene expression regulation; regulatory sequences and mutation; chromatin and epigeneticsSix 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)4View Syllabus
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111MCB (MCELLBI) 206Physical BiochemistryProtein structure; protein evolutionApplication of modern physical concepts and experimental methods to the analysis of the structure, function, and interaction of large molecules of biological interestSpring 20153View Syllabusmeet the Advanced Topics requirement MCB
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111MCB (MCELLBI) 210Macromolecular Reactions and the CellDNA replication/repair and mutation; gene expression regulation; chromatin and epigenetics; biochemistry methodologyGeneral 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)4View Syllabusmeet the Advanced Topics requirement and highly recommended MCB
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111PMB (PLANTBI) C244 / Bio Eng C244Introduction to Protein InformaticsProtein evolution; structural bioinformaticsThis 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 20144
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1Molecular Genetics and Genomics12
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112MCB (MCELLBI) 140General GeneticsInheritance 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)4https://mcb.berkeley.edu/courses/syllabi/mcb140_fall2013.pdf
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112PMB (PLANTBI) C148 / MCB (MCELLBI) C148Microbial Genomics and GeneticsMicrobial genomics; comparative genomics; metagenomicsbacterial 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
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112IB (INTEGBI) 164Human Genetics and GenomicsPopulation genetics; molecular evolutionThis 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)
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112MCB (MCELLBI) 200A-B (two courses)Fundamentals of Molecular and Cell BiologyDNA replication/repair and mutation; inheritance and mutation; gene expression regulation; regulatory sequences and mutation; chromatin and epigeneticsSix 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)4View Syllabus
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112MCB (MCELLBI) 240Advanced Genetic AnalysisInheritance and mutation; regulatory sequences and mutations; genetics methodologyPrinciples 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 2015View Syllabushttps://mcb.berkeley.edu/courses/mcb240/
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12CS 294 / COMPBIO 290Special topics course in computational molecular biologyAlgorithms 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 20153
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113Stat C245EStatistical Genomics I and II.Population genetics; phylogenetics; high-throughput sequencingC245E 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
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113Stat C245EFStatistical Genomics I and II.Population genetics; phylogenetics; high-throughput sequencingC245E 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)
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12MCB (MCELLBI) C243*Seq: Methods and ApplicationsHigh-throughput sequencingA 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 20143View Syllabus
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PB HLTH 256AHuman Genome, Environment and Health
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PB HLTH 256B (PH256A must be taken concurrently)Genetic and Genomic Analysis
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1Population Genetics and Genomics13
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113IB (INTEGBI) 161Population and Evolutionary GeneticsPopulation genetics; molecular evolutionstudy 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
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113IB (INTEGBI) 163/ MCB (MCELLBI)142Molecular and Genomic EvolutionPopulation genetics; population genomics; inheritance and mutation; molecular evolutionintroduce 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)4http://mcb.berkeley.edu/courses/mcb142/
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113IB (INTEGBI) 164Human Genetics and GenomicsPopulation genetics; molecular evolutionThis 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
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113IB (INTEGBI) 206Statistical PhylogeneticsPhylogeneticsEvolutionary 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
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113Stat C245EStatistical Genomics I and II.Population genetics; phylogenetics; high-throughput sequencingC245E 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
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113Stat C245EFStatistical Genomics I and II.Population genetics; phylogenetics; high-throughput sequencingC245E 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)
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113Bio Eng 241Probabilistic Modeling in Computational BiologyPhylogenetics; phylogenomicsthe reconstruction of ancient genes and genomes by reverse Bayesian inference under various stochastic models of molecular evolution.Spring 2014 (spring only)4https://schedulebuilder.berkeley.edu/explore/courses/SP/2012/5258
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Cluster B: Computational2
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2Algorithms and software21
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221CS 170Introduction to CS TheoryBasics of algorithms, Graph algoriothms, complexityBasics of algorithms, Graph algoriothms, complexityFall, Spring 2014-15 (offered both semesters)4
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221CS 270Combinatorial Algorithms and Data StructuresLinear programming; semi-definite programmingDesign 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 problemsSpring 20154https://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.
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221CS 176Algorithms in Computational BiologySequence 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 modelsFall 2014 (fall only)4
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221Bio Eng 231 / 131Introduction to Computational Molecular and Cellular BiologySequence alignment, phylogenetics RNA structure predictioncomputational 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 givenFall 2013 (fall only)4
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221CS 169Software EngineeringHandling large software projects; cloud computingdesigning, 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)4https://sites.google.com/site/ucbsaas/
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221CS 267Applications of Parallel ComputersdWorking with shared memory, multiple cpusprogram 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 datasetsSpring 2015 (spring only)?http://www.cs.berkeley.edu/~demmel/cs267_Spr14/
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221CS 286BImplementation of database systemsdatabase managementadvanced 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 analyticsFall 20144http://www.cs286.net/home/syllabus
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2Statistics and Probability22
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222Stat 134/EE 126Probability and Random ProcessesBasic probability,distributions, random variables, markov chains, Poisson processes, conditional probabilitiesprovides 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)3http://www.stat.berkeley.edu/~aldous/134/ ; https://inst.eecs.berkeley.edu/~ee126/fa07/http://www-inst.eecs.berkeley.edu/~ee126/fa13/
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222Stat 135Concepts of StatisticsMax Likelihood, ANOVAA 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)4http://www.stat.berkeley.edu/~nolan/stat135/Spr03/index.html
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222Stat 150Stochastic ProcessesStochastic ProcessesRandom 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 processesFall, Spring 2014-15 (generally offered only in the spring)3http://www.stat.berkeley.edu/~aldous/150/
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222CS 174Combinatorics and Discrete ProbabilityBasics of algorithms, Graph theory, complexityPermutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychev’s inequality, Chernov bounds. Birthday paradox, coupon collector’s problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence boundsSpring 20154http://www.cs.berkeley.edu/~jordan/courses/174-spring15/Combinatorics and Discrete Probability
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222Stat 201A/B (required course)Advanced intro to probabilityDistributions 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)4http://statistics.berkeley.edu/courses/fall-2013/fall-2013-stat-201a-001-lec
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222Stat 210ATheoretical Statisticsintroduction 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)4http://www.cs.berkeley.edu/~jordan/courses/210A-fall14/
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222Stat 215 AStatistical Models: Theory and ApplicationExploratory data analysis; Regression; GLMApplied 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
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222Stat 215 BStatistical Models: Theory and ApplicationExploratory data analysis; Regression; GLMApplied 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
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222Stat C205A/ Math 218A-BProbability TheoryDesigned 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)4http://www.stat.berkeley.edu/~aldous/205A/
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222Stat C205B/ Math 218A-BProbability TheoryDesigned 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)4http://www.stat.berkeley.edu/~aldous/205A/
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222Stat 204Probability for ApplicationsIn 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
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2Machine Learning and optimization23
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223CS 189/ CS 289AIntroduction to machine learningclustering, dimensionality redeuction, classification, regressionBasic machine learning (clustering, dimensionality redeuction, classification, regression)Spring 2015 (spring only)4http://inst.eecs.berkeley.edu/~cs189/sp15/
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223CS 281A/ Stat 241AStatistical Learning TheoryProbabilistic graphical modelsprobabilistic 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 theoryFall 20143http://www.cs.berkeley.edu/~jordan/courses/281A-spring14/
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223CS 281B / Stat 241BStatistical Learning TheoryPrediction methodsintroduction 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)3http://www.stat.berkeley.edu/~bartlett/courses/2014spring-cs281bstat241b/
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223EE 127/ 227ATOptimization Models and ApplicationsConvex 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 optimizationSpring 20154http://www.eecs.berkeley.edu/~elghaoui/Teaching/EE127/ScheduleNew.pdf
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223EE 227BConvex optimizationConvex optimization: duality, roustnessConvex optimization: convexity, conic optimization, duality.
Selected topics: robustness, stochastic programming, applications
Fall 20154http://www.eecs.berkeley.edu/~elghaoui/Teaching/EE227BT/
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2Mathematical modeling and scientific computing24
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224EE 219ANumerical Simulation and ModelingNumerical Simulation and ModelingFundamental 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)3http://potol.eecs.berkeley.edu/classWiki/tiki-index.php?page=EECS219A-Fall-2014
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224Math 128ANumerical AnalysisModeling and solving ODE systemsBasic 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)4http://persson.berkeley.edu/128A/
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224Math 228ANumerical Solutions of Differential EquationsModeling and solving DE systemsnumerical solutions and theoretical treatment of differential equations and integral equationsFall 2014 (fall only)4https://math.berkeley.edu/~mgu/MA228A/index.html
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224EE 221ALinear system theoryProperties 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 20144https://inst.eecs.berkeley.edu/~ee221a/fa14/
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224EE 222Nonlinear Systems--Analysis, Stability and ControlIntroduction 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)3http://www-inst.eecs.berkeley.edu/~ee222/sp15/
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Other possible for algorithms:
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CS 271RANDOMNESS & COMPUTATIONhttp://www.cs.berkeley.edu/~sinclair/cs271/f11.htmlFall 2011
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Stat 243Introduction to statistical computinghttp://www.stat.berkeley.edu/~paciorek/teaching/teaching.htmlFall 2014 (fall only)
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EE 226ARandom Processes in Systemshttps://inst.eecs.berkeley.edu/~ee226a/fa09/Fall 2014 (fall only)
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Background courses:
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Math 110 or EE 127Linear algebraSpring 2015 (spring only)
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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)
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CS 61BData Structureshttp://cs61b.ug/sp16/see description above
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CS 70Discrete Mathematics and Probability Theoryhttp://www.eecs70.org/
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CS 164Programming Languages and CompilersHow comilers workIntroduction 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 PythonFall, Spring 2014-15 (offered both semesters)
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IB (INTEGBI) 160EvolutionAn 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 researchFall 2014 (fall only)
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Key links
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http://bioeng.berkeley.edu/undergrad/program/bioetopics
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https://schedulebuilder.berkeley.edu/explore/courses/
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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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http://www.eecs.berkeley.edu/education/courses.shtml
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