Information and Data Sciences

## Course Listings

**IDS 9. Introduction to Information and Data Systems Research. ***1 unit (1-0-0); second term. *This course will introduce students to research areas in IDS through weekly overview talks by Caltech faculty and aimed at first-year undergraduates. Others may wish to take the course to gain an understanding of the scope of research in computer science. Graded pass/fail. Not offered 2019–20.

**ACM/IDS 101 ab. Methods of Applied Mathematics. ***12 units (4-4-4). *For course description, see Applied and Computational Mathematics.

**ACM/IDS 104. Applied Linear Algebra. ***9 units (3-1-5). *For course description, see Applied and Computational Mathematics.

**CMS/ACM/IDS 107. Linear Analysis with Applications. ***12 units (3-3-6). *For course description, see Computing and Mathematical Sciences.

**CMS/ACM/IDS 113. Mathematical Optimization. ***12 units (3-0-9). *For course description, see Computing and Mathematical Sciences.

**ACM/EE/IDS 116. Introduction to Probability Models. ***9 units (3-1-5). *For course description, see Applied and Computational Mathematics.

**CS/IDS 121. Relational Databases. ***9 units (3-0-6). *For course description, see Computer Science.

**EE/Ma/CS/IDS 127. Error-Correcting Codes. ***9 units (3-0-6). *For course description, see Electrical Engineering.

**EE/Ma/CS/IDS 136. Topics in Information Theory. ***9 units (3-0-6). *For course description, see Electrical Engineering.

**CMS/CS/IDS 139. Analysis and Design of Algorithms. ***12 units (3-0-9). *For course description, see Computation and Mathematical Sciences.

**CS/IDS 142. Distributed Computing. ***9 units (3-0-6). *For course description, see Computer Science.

**CS/EE/IDS 143. Communication Networks. ***9 units (3-3-3). *For course description, see Computer Science.

**Ma/ACM/IDS 140 ab. Probability. ***9 units (3-0-6); second, third terms. *For course description, see Mathematics.

**CMS/CS/EE/IDS 144. Networks: Structure & Economics. ***12 units (3-4-5). *For course description, see Computing and Mathematical Sciences.

**CS/IDS 150 ab. Probability and Algorithms. ***9 units (3-0-6). *For course description, see Computer Science.

**CS/IDS 153. Current Topics in Theoretical Computer Science. ***9 units (3-0-6). *For course description, see Computer Science.

**ACM/IDS 154. Inverse Problems and Data Assimilation. ***9 units (3-0-6). *For course description, see Applied and Computational Mathematics.

**CMS/CS/CNS/EE/IDS 155. Machine Learning & Data Mining. ***12 units (3-3-6). *For course description, see Computing and Mathematical Sciences.

**IDS/ACM/CS 157. Statistical Inference. ***9 units (3-2-4); third term. **Prerequisites: ACM/EE/IDS 116, Ma 3.* Statistical Inference is a branch of mathematical engineering that studies ways of extracting reliable information from limited data for learning, prediction, and decision making in the presence of uncertainty. This is an introductory course on statistical inference. The main goals are: develop statistical thinking and intuitive feel for the subject; introduce the most fundamental ideas, concepts, and methods of statistical inference; and explain how and why they work, and when they don’t. Topics covered include summarizing data, fundamentals of survey sampling, statistical functionals, jackknife, bootstrap, methods of moments and maximum likelihood, hypothesis testing, p-values, the Wald, Student’s t-, permutation, and likelihood ratio tests, multiple testing, scatterplots, simple linear regression, ordinary least squares, interval estimation, prediction, graphical residual analysis. Instructor: Zuev.

**IDS/ACM/CS 158. Fundamentals of Statistical Learning. ***9 units (3-3-3); third term. **Prerequisites: Ma 3 or ACM/EE/IDS 116, IDS/ACM/CS 157.* The main goal of the course is to provide an introduction to the central concepts and core methods of statistical learning, an interdisciplinary field at the intersection of statistics, machine learning, information and data sciences. The course focuses on the mathematics and statistics of methods developed for learning from data. Students will learn what methods for statistical learning exist, how and why they work (not just what tasks they solve and in what built-in functions they are implemented), and when they are expected to perform poorly. The course is oriented for upper level undergraduate students in IDS, ACM, and CS and graduate students from other disciplines who have sufficient background in probability and statistics. The course can be viewed as a statistical analog of CMS/CS/CNS/EE/IDS 155. Topics covered include supervised and unsupervised learning, regression and classification problems, linear regression, subset selection, shrinkage methods, logistic regression, linear discriminant analysis, resampling techniques, tree-based methods, support-vector machines, and clustering methods. Instructor: Zuev.

**CS/CNS/EE/IDS 159. Advanced Topics in Machine Learning. ***9 units (3-0-6). *For course description, see Computer Science.

**EE/CS/IDS 160. Fundamentals of Information Transmission and Storage. ***9 units (3-0-6). *For course description, see Electrical Engineering.

**CS/IDS 162. Data, Algorithms and Society. ***9 units (3-0-6). *For course description, see Computer Science.

**CS/CNS/EE/IDS 165. Foundations of Machine Learning and Statistical Inference. ***12 units (3-3-6). *For course description, see Computer Science.

**EE/CS/IDS 167. Introduction to Data Compression and Storage. ***9 units (3-0-6). *For course description, see Electrical Engineering.

**ACM/EE/IDS 170. Mathematics of Signal Processing. ***12 units (3-0-9). *For course description, see Applied and Computational Mathematics.

**CS/IDS 178. Numerical Algorithms and their Implementation. ***9 units (3-3-3). *For course description, see Computer Science.

**IDS 197. Undergraduate Reading in the Information and Data Sciences. ***Units are assigned in accordance with work accomplished; first, second, third terms. **Prerequisites: Consent of supervisor is required before registering. *Supervised reading in the information and data sciences by undergraduates. The topic must be approved by the reading supervisor and a formal final report must be presented on completion of the term. Graded pass/fail. Instructor: Staff.

**IDS 198. Undergraduate Projects in Information and Data Sciences. ***Units are assigned in accordance with work accomplished; first, second, third terms. **Prerequisites: Consent of supervisor is required before registering. *Supervised research in the information and data sciences. The topic must be approved by the project supervisor and a formal report must be presented upon completion of the research. Graded pass/fail. Instructor: Staff.

**IDS 199. Undergraduate thesis in the Information and Data Sciences. ***9 units (1-0-8); first, second, third terms. **Prerequisites: instructor’s permission, which should be obtained sufficiently early to allow time for planning the research. *Individual research project, carried out under the supervision of a faculty member and approved by the option representative. Projects must include significant design effort and a written Report is required. Open only to upperclass students. Not offered on a pass/fail basis. Instructor: Staff.

**ACM/IDS 204. Topics in Linear Algebra and Convexity. ***12 units (3-0-9). *For course description, see Applied and Computational Mathematics.

**ACM/IDS 213. Topics in Optimization. ***9 units (3-0-6). *For course description, see Applied and Computational Mathematics.

**ACM/IDS 216. Markov Chains, Discrete Stochastic Processes and Applications. ***9 units (3-0-6). *For course description, see Applied and Computational Mathematics.

**ACM/EE/IDS 217. Advanced Topics in Stochastic Analysis. ***9 units (3-0-6). *For course description, see Applied and Computational Mathematics.