Term 
Course Title and Course Number 
Insturctor 
Number of Units 
Textbook 
Note 
Spring 2021 
Math 202B: Introduction to Topology and Analysis (Second Course) 
Prof. Michael Christ 
4 
 Gerald Folland, Real Analysis: Modern Techniques and Their Applications

Second course of Berkeley graduate sequence in analysis.
Mainly covers Lebesgue theory in ndimensions and functional analysis. 
Spring 2021 
COMPSCI 189: Introduction to Machine Learning 
Prof. Jonathan Shewchuk 
4 
 [JWHT] An Introduction to Statistical Learning
 [HTF] The Elements of Statistical Learning

Fundamental machine learning techniques: regression methods, GDA, decision trees
/ random forests,
(convolutional) neural networks, dimensionality reduction, clustering. 
Spring 2021 
DATA 100: Principles and Techniques of Data Science 
Prof. Joseph E. Gonzalez et al. 
4 

Data cleaning, data visualization, introductory machine learning techniques. 
Spring 2021 
Math 185: Complex Analysis 
Prof. Di Fang 
4 
 [BC] Complex Variables and Applications

Analytic functions of a complex variable. Cauchy's integral theorem, power series, Laurent series,
singularities of analytic functions, the residue theorem with application to definite integrals. Some
additional topics such as conformal mapping. 
Fall 2020 
Math 202A: Introduction to Topology and Analysis (First Course) 
Prof. Alan Hammond 
4 
 Richard F. Bass, Real Analysis for Graduate Students
 Patrick Billingsley, Probability and measure
 Gerald Folland, Real Analysis: Modern Techniques and Their Applications
 Terrence Tao, An Introduction to Measure Theory

First course of Berkeley graduate sequence in analysis.
Mainly covers introductory measure theory. 
Fall 2020 
Math 222A: Partial Differential Equations (First Course) 
Prof. Daniel Tataru 
4 
 Lawrence C. Evans, Partial differential equations
 [FJ], Introduction to the
Theory of Distributions
 Lars Hörmander, The Analysis of
Linear Partial Differential Operators
 Robert Strichartz, A Guide to Distribution
Theory and Fourier Transforms

First course of Berkeley graduate sequence in partial differential equations.
Mainly covers introductory theory of
distributions and functional analysis of PDEs. 
Fall 2020 
STAT 243: Introduction to Statistical Computing 
Chris Paciorek 
4 
 [NR], Learning the bash Shell
 Joseph Adler, R in a Nutshell
 Hadley Wickham, Advanced R
 James E. Gentle, Computational Statistics

Concepts in statistical programming and statistical computation,
including programming principles, data and text manipulation, parallel processing,
MonteCarlo simulation, numerical linear algebra, and optimization. 
Spring 2020 
EECS 126: Probability and Random Processes 
Prof. Kannan Ramchandran

4 
 [BT], Introduction to Probability, 2nd Edition (2008)
 [W] Probability in Electrical Engineering and Computer Science:
An ApplicationDriven Course (2014)

This course covers the fundamentals of probability and random processes useful in fields such as
networks,
communication, signal processing, and control. Sample space, events, probability law.
Conditional probability. Independence. Random variables. Distribution, density functions.
Random vectors. Law of large numbers. Central limit theorem. Estimation and detection.
Markov chains. 
Spring 2020 
MATH 221: Advanced Matrix Computations 
Prof. James Demmel

4 ( view project ) 
 [J. Demmel], Applied Numerical Linear Algebra (1997)

Direct solution of linear systems, including
large sparse systems: error bounds, iteration methods,
least square approximation, eigenvalues and eigenvectors
of matrices, nonlinear equations, and minimization of functions. 
Spring 2020 
MATH 228B: Numerical Solution to Partial Differential Equations 
Prof. Sunčica Čanić

4 (A+) 
 [R. J. LeVeque], Finite Difference Methods for Ordinary
and Partial Differential Equations,
Steady State and Time Dependent Problems (2007)
 [R. J. LeVeque], Finite Volume Methods for Hyperbolic Problems (2002)
 [C. Johnson], Numerical Solution of Partial
Differential Equations by the Finite Element Method (2009)

Theory and practical methods for numerical solution of partial differential
equations. Finite difference methods for elliptic, parabolic
and hyperbolic equations, stability, accuracy and convergence,
von Neumann analysis and CFL conditions. Finite volume methods
for hyperbolic conservation laws, finite element methods for
elliptic and parabolic equations, discontinuous Galerkin methods
for conservation laws. Other topics include applications of the
techniques to a range of problems. 
Fall 2019 
COMPSCI 61B: Data Structures 
Prof. Paul Hilfinger

4 (A+) 
 [Paul N. Hilfinger], A Java Reference
 [Sierra & Bates], Head First Java
 [Paul N. Hilfinger], Data Structures (Into Java)

Fundamental dynamic data structures,
including linear lists, queues,
trees, and other linked structures;
arrays strings, and hash tables.
Storage management. Elementary
principles of software engineering.
Abstract data types. Algorithms for
sorting and searching. Introduction
to the Java programming language. 
Fall 2019 
MATH 104: Introduction to Mathematical Analysis 
Prof. Rui Wang

4 
 [Rudin], Principles of mathematical analysis

The real number system. Sequences, limits,
and continuous functions in R and R.
The concept of a metric space. Uniform
convergence, interchange of limit operations.
Infinite series. Mean value theorem and applications.
The Riemann integral. 
Fall 2019 
MATH 228A: Numerical Solution to Ordinary Differential Equations 
Prof. Lin Lin 
4 
 [R.J. LeVeque] Finite Difference Methods
for Ordinary and Partial Differential
Equations: SteadyState and Timedependent Problems
 [R.J. LeVeque] A First Course in
the Numerical Analysis of Differential Equations
 [Syvert Paul Nørsett,
Gerhard Wanner, Ernst Hairer] Solving
Ordinary Differential Equations I: Nonstiff Problems

Ordinary differential equations: RungeKutta
and predictorcorrector methods; stability
theory, Richardson extrapolation, stiff equations,
boundary value problems. Partial differential
equations: stability, accuracy and convergence,
Von Neumann and CFL conditions, finite difference
solutions of hyperbolic and parabolic equations.
Finite differences and finite element solution
of elliptic equations. 
Spring 2019 
COMPSCI 61A: Struture and Interpretation of Computer Programs 
Prof. Daniel Garcia

4 

An introduction to programming and computer science focused on abstraction techniques as means to
manage program complexity. Techniques include procedural abstraction; control abstraction using
recursion, higherorder functions, generators, and streams; data abstraction using interfaces, objects,
classes, and generic operators; and language abstraction using interpreters and macros. The course
exposes students to programming paradigms, including functional, objectoriented, and declarative
approaches. It includes an introduction to asymptotic analysis of algorithms. There are several
significant programming projects. 
Spring 2019 
MATH 113: Abstract Algebra 
Prof. Sylvie Corteel

4 
 [John B. Fraleigh] A First Course in Abstract Algebra

Sets and relations. The integers, congruences, and the Fundamental Theorem of Arithmetic. Groups and
their factor groups. Commutative rings, ideals, and quotient fields. The theory of polynomials:
Euclidean algorithm and unique factorizations. The Fundamental Theorem of Algebra. Fields and field
extensions. 
Spring 2019 
MATH 128A: Numerical Analysis 
Prof. John Strain

4 
 [Richard L. Burden, J. Douglas Faires] Numerical Analysis

MATLAB programming for numerical calculations, roundoff error, approximation and interpolation,
numerical quadrature, and solution of ordinary differential equations. 
Spring 2019 
UGBA 103: Introduction to Corporate Finance 
Prof. Christine A. Parlour, Matteo Benetton

4 (A+) 
 [Jonathan B. Berk, Peter DeMarzo] Corporate Finance

Analysis and management of the flow of funds through an enterprise. Cash management, source and
application of funds, term loans, types and sources of longterm capital. Capital budgeting, cost of
capital, and financial structure. Introduction to capital markets. 
Fall 2018 
MATH 110: Linear Algebra 
Prof. Sug Woo Shin

4 
 [Arnold J. Insel, et al.] Linear Algebra

Matrices, vector spaces, linear transformations, inner products, determinants. Eigenvectors. QR
factorization. Quadratic forms and Rayleigh's principle. Jordan canonical form, applications. Linear
functionals. 
Fall 2018 
History 124A: The Recent United States:
The United States from the Late 19th Century
to the Eve of World War II 
Prof. Brendan A. Shanahan

4 

During the first halfcentury before World War II, the United States became an industrialized, urban society with national markets and communication media. This class will explore in depth some of the most important changes and how they were connected. We will also examine what did not change, and how state and local priorities persisted in many arenas. Among the topics addressed: population movements and efforts to control immigration; the growth of corporations and trade unions; the campaign for women's suffrage; Prohibition; an end to child labor; the institution of the Jim Crow system; and the reshaping of higher education.
