Computing and Mathematical Sciences
CMS/ACM 107. Linear Analysis with Applications. 12 units (3-3-6); first term. Prerequisites: ACM 104 or equivalent, Ma 1b or equivalent. Covers the basic algebraic, geometric, and topological properties of normed linear spaces, inner-product spaces, and linear maps. Emphasis is placed both on rigorous mathematical development and on applications to control theory, data analysis and partial differential equations. Instructor: Stuart.
CMS/ACM 113. Mathematical Optimization. 9 units (3-0-6); first term. Prerequisites: ACM 95/100 ab, ACM 11, or instructor’s permission. Corequisite: It is suggested that students take ACM 104 concurrently. This class studies mathematical optimization from the viewpoint of convexity. Topics covered include duality and representation of convex sets; linear and semidefinite programming; connections to discrete, network, and robust optimization; relaxation methods for intractable problems; as well as applications to problems arising in graphs and networks, information theory, control, signal processing, and other engineering disciplines. Instructor: Chandrasekaran.
CMS/ACM/EE 117. Probability and Random Processes. 12 units (3-0-9); first term. Prerequisites: ACM 104 and ACM/EE 116. The course will start with a quick reminder on probability spaces, discrete and continuous random variables. It will cover the following core topics: branching processes, Poisson processes, limit theorems, Gaussian variables, vectors, spaces, processes and measures, the Brownian motion, Gaussian learning, game theory and decision theory (finite state space), martingales (concentration, convergence, Doob’s inequalities, optional/optimal stopping, Snell’s envelope), large deviations (introduction, if time permits). Instructor: Owhadi.
CMS/CS 139. Analysis and Design of Algorithms. 12 units (3-0-9); second term. Prerequisites: Ma 2, Ma 3, Ma/CS 6a, CS 21, CS 38/138, CMS/ACM/EE 116, or instructor’s permission. This course covers advanced topics in the design and analysis of algorithms. Topics are drawn from approximation algorithms, randomized algorithms, online algorithms, streaming algorithms, and other areas of current research interest in algorithms. Instructor: Vidick.
CMS/CS/EE 144. Networks: Structure Economics. 12 units (3-3-6); second term. Prerequisites: Ma 2, Ma 3, Ma/CS 6a, and CS 38, or instructor permission. Social networks, the web, and the internet are essential parts of our lives and we all depend on them every day, but do you really know what makes them work? This course studies the “big” ideas behind our networked lives. Things like, what do networks actually look like (and why do they all look the same)? How do search engines work? Why do memes spread the way they do? How does web advertising work? For all these questions and more, the course will provide a mixture of both mathematical analysis and hands-on labs. The course assumes students are comfortable with graph theory, probability, and basic programming. Instructor: Wierman.
CMS/CS/CNS/EE 155. Machine Learning Data Mining. 12 units (3-3-6); second term. Prerequisites: background in algorithms and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156 a or instructor’s permission). This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. This course will also cover core foundational concepts underpinning and motivating modern machine learning and data mining approaches. This course will be research-oriented, and will cover recent research developments. Instructor: Yue.
CMS 290 abc. Computing and Mathematical Sciences Colloquium. 1 unit; first, second, third terms. Registration is limited to graduate students in the CMS department only. This course is a research seminar course covering topics at the intersection of mathematics, computation, and their applications. Students are asked to attend one seminar per week (from any seminar series on campus) on topics related to computing and mathematical sciences. This course is a requirement for first-year PhD students in the CMS department. Instructor: Staff.
CMS 300. Research in Computing and Mathematical Sciences. Hours and units by arrangement. Research in the field of computing and mathematical science. By arrangement with members of the staff, properly qualified graduate students are directed in research. Instructors: Staff.