INTRO-COMPUT SCI/PROG IN JAVA

A general introduction to computer science for science and engineering students interested in majoring in computer science or engineering. Covers fundamental concepts of computer science, algorithmic problem-solving capabilities, and introductory Java programming skills. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: 1004 or 1005.

• TR 11:40 a.m.-12:55 p.m.
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 3 points

INTRO TO COMP FOR ENG/APP SCI

An interdisciplinary course in computing intended for first year SEAS students. Introduces computational thinking, algorithmic problem solving and Python programming with applications in science and engineering. Assumes no prior programming background.

• TR 5:40 p.m.-6:55 p.m.
• Instructor: Timothy K Paine
  CULPA: 4 Info
• 3 points

EMERGING SCHOLARS PROG SEMINAR

Peer-led weekly seminar intended for first and second year undergraduates considering a major in Computer Science. Pass/fail only. May not be used towards satisfying the major or SEAS credit requirements.

• F 8:40 a.m.-9:55 a.m.
• F 10:10 a.m.-11:25 a.m.
• F 11:40 a.m.-12:55 p.m.
• F 1:10 p.m.-2:25 p.m.
• F 2:40 p.m.-3:55 p.m.
• F 4:10 p.m.-5:25 p.m.
• F 9:30 a.m.-10:45 a.m.
• F 11 a.m.-12:15 p.m.
• F 12:30 p.m.-1:45 p.m.
• F 2 p.m.-3:15 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 1 points

DEVELOPMENT TECHNOLOGY

FULL STACK WEB DEVELOP

Introduction to software development tools and environments. Each section devoted to a specific tool or environment. One-point sections meet for two hours each week for half a semester, and two point sections include an additional two-hour lab.

• F 6:10 p.m.-8 p.m.
• Instructor: Shoaib Ahamed
  Info
• 2 points

Intermediate Computing in Python

INTERMDTE COMPUTING IN PY

Essential data structures and algorithms in Python with practical software development skills, applications in a variety of areas including biology,  natural language processing, data science and others.

• F 1:10 p.m.-3:40 p.m.
• Instructor: Jan Janak
  Info
• 4 points

DATA STRUCTURES IN JAVA

Data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Rudiments of the analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134, COMS W3136, COMS W3137.

• MW 4:10 p.m.-5:25 p.m.
• MW 5:40 p.m.-6:55 p.m.
• Instructor: Brian Borowski
  Info
• 3 points

ADVANCED PROGRAMMING

C programming language and Unix systems programming. Also covers Git, Make, TCP/IP networking basics, C++ fundamentals.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Jae W Lee
  CULPA: 42 Info
• 4 points

DISCRETE MATHEMATICS

Logic and formal proofs, sequences and summation, mathematical induction, binomial coefficients, elements of finite probability, recurrence relations, equivalence relations and partial orderings, and topics in graph theory (including isomorphism, traversability, planarity, and colorings).

• TR 10:10 a.m.-11:25 a.m.
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Ansaf Salleb-Aouissi
  CULPA: 21 h-index: 16 Info
• 4 points

COMPUTER SCIENCE THEORY

Regular languages: deterministic and non-deterministic finite automata, regular expressions. Context-free languages: context-free grammars, push-down automata. Turing machines, the Chomsky hierarchy, and the Church-Turing thesis. Introduction to Complexity Theory and NP-Completeness.

• MW 2:40 p.m.-3:55 p.m.
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Josh Alman
  Info
• Instructor: Mihalis Yannakakis
  CULPA: 14 Wikipedia h-index: 93 Info
• 3 points

FUNDAMENTALS OF COMPUTER SYSTS

Fundamentals of computer organization and digital logic. Boolean algebra, Karnaugh maps, basic gates and components, flipflops and latches, counters and state machines, basics of combinational and sequential digital design. Assembly language, instruction sets, ALU’s, single-cycle and multi-cycle processor design, introduction to pipelined processors, caches, and virtual memory.

• TR 10:10 a.m.-11:25 a.m.
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Daniel S Rubenstein
  CULPA: 29 Info
• 3 points

INTRODUCTION TO DATABASES

The fundamentals of database design and application development using databases: entity-relationship modeling, logical design of relational databases, relational data definition and manipulation languages, SQL, XML, query processing, physical database tuning, transaction processing, security. Programming projects are required.

• MW 2:40 p.m.-3:55 p.m.
• F 10:10 a.m.-12:40 p.m.
• Instructor: Kenneth A Ross
  Info
• Instructor: Donald F Ferguson
  CULPA: 17 Wikipedia Info
• 3 points

PROGRAMMING LANG & TRANSLATORS

Modern programming languages and compiler design. Imperative, object-oriented, declarative, functional, and scripting languages. Language syntax, control structures, data types, procedures and parameters, binding, scope, run-time organization, and exception handling. Implementation of language translation tools including compilers and interpreters. Lexical, syntactic and semantic analysis; code generation; introduction to code optimization. Teams implement a language and its compiler.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Ronghui Gu
  h-index: 11 Info
• 3 points

OPERATING SYSTEMS I

Design and implementation of operating systems. Topics include process management, process synchronization and interprocess communication, memory management, virtual memory, interrupt handling, processor scheduling, device management, I/O, and file systems. Case study of the UNIX operating system. A programming project is required.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Kostis Kaffes
  Info
• 3 points

COMPUTER NETWORKS

Introduction to computer networks and the technical foundations of the Internet, including applications, protocols, local area networks, algorithms for routing and congestion control, security, elementary performance evaluation. Several written and programming assignments required.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Xia Zhou
  Info
• 3 points

COMPUTER SYSTEMS FOR DATA SCIENCE

An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers.

• R 7 p.m.-9:30 p.m.
• 3 points

COMPUTER GRAPHICS

Introduction to computer graphics. Topics include 3D viewing and projections, geometric modeling using spline curves, graphics systems such as OpenGL, lighting and shading, and global illumination. Significant implementation is required: the final project involves writing an interactive 3D video game in OpenGL.

• TR 7:10 p.m.-8:25 p.m.
• Instructor: Hadi Fadaifard
  Info
• 3 points

COMPUTER ANIMATION

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Changxi Zheng
  h-index: 30 Info
• 3 points

USER INTERFACE DESIGN

Introduction to the theory and practice of computer user interface design, emphasizing the software design of graphical user interfaces. Topics include basic interaction devices and techniques, human factors, interaction styles, dialogue design, and software infrastructure. Design and programming projects are required.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Lydia Chilton
  CULPA: 3 h-index: 18 Info
• 3 points

3D UI AND AUGMENTED REALITY

Design, development, and evaluation of 3D user interfaces. Interaction techniques and metaphors, from desktop to immersive. Selection and manipulation. Travel and navigation. Symbolic, menu, gestural, and multimodal interaction. Dialogue design. 3D software support. 3D interaction devices and displays. Virtual and augmented reality. Tangible user interfaces. Review of relevant 3D math.

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Steven K Feiner
  CULPA: 18 Wikipedia Info
• 3 points

SECURITY II

Advanced security. Centralized, distributed, and cloud system security. Cryptographic protocol design choices. Hardware and software security techniques. Security testing and fuzzing. Blockchain. Human security issues. Note: May not earn credit for both W4182 and W4180 or W4187.

• F 1:10 p.m.-3:40 p.m.
• Instructor: John S Koh
  Info
• 3 points

GRAPH THEORY

• W 7 p.m.-9:30 p.m.
• Instructor: Yihao Zhang
  Info
• 3 points

Networks, Crowds, and the Web

NETWORKS CROWDS AND THE W

Introduces fundamental ideas and algorithms on networks of information collected by online services. It covers properties pervasive in large networks, dynamics of individuals that lead to large collective phenomena, mechanisms underlying the web economy, and results and tools informing societal impact of algorithms on privacy, polarization and discrimination.

• TR 4:10 p.m.-5:25 p.m.
• Instructor: Augustin Chaintreau
  CULPA: 6 h-index: 33 Info
• 3 points

ANALYSIS OF ALGORITHMS I

Prerequisites: (COMS W3134 or COMS W3136COMS W3137) and (COMS W3203) Introduction to the design and analysis of efficient algorithms. Topics include models of computation, efficient sorting and searching, algorithms for algebraic problems, graph algorithms, dynamic programming, probabilistic methods, approximation algorithms, and NP-completeness.

• TR 11:40 a.m.-12:55 p.m.
• TR 10:10 a.m.-11:25 a.m.
• Instructor: Eleni Drinea
  CULPA: 8 Info
• Instructor: Christos H Papadimitriou
  CULPA: 11 h-index: 129 Info
• 3 points

Advanced Algorithms

Introduces classic and modern algorithmic ideas that are central to many areas of Computer Science. The focus is on most powerful paradigms and techniques of how to design algorithms, and how to measure their efficiency. The intent is to be broad, covering a diversity of algorithmic techniques, rather than be deep. The covered topics have all been implemented and are widely used in industry. Topics include: hashing, sketching/streaming, nearest neighbor search, graph algorithms, spectral graph theory, linear programming, models for large-scale computation, and other related topics

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Alexandr Andoni
  CULPA: 2 h-index: 34 Info
• 3 points

PRIN-INNOVATN/ENTREPRENEURSHIP

Team project centered course focused on principles of planning, creating, and growing a technology venture. Topics include: identifying and analyzing opportunities created by technology paradigm shifts, designing innovative products, protecting intellectual property, engineering innovative business models.

• MW 8:40 a.m.-9:55 a.m.
• Instructor: William Reinisch
  CULPA: 1 Info
• 3 points

ARTIFICIAL INTELLIGENCE

Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits.

• MW 2:40 p.m.-3:55 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Tony B Dear
  CULPA: 17 Info
• 3 points

NATURAL LANGUAGE PROCESSING

Computational approaches to natural language generation and understanding. Recommended preparation: some previous or concurrent exposure to AI or Machine Learning. Topics include information extraction, summarization, machine translation, dialogue systems, and emotional speech. Particular attention is given to robust techniques that can handle understanding and generation for the large amounts of text on the Web or in other large corpora. Programming exercises in several of these areas.

• MW 2:40 p.m.-3:55 p.m.
• F 10:10 a.m.-12:40 p.m.
• Instructor: Daniel Bauer
  CULPA: 17 h-index: 13 Info
• 3 points

MACHINE LEARNING FOR DATA SCI

• F 1:10 p.m.-3:40 p.m.
• 3 points

Computer Vision II: Learning

Advanced course in computer vision. Topics include convolutional networks and back-propagation, object and action recognition, self-supervised and few-shot learning, image synthesis and generative models, object tracking, vision and language, vision and audio, 3D representations, interpretability, and bias, ethics, and media deception.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Carl Vondrick
  CULPA: 1 h-index: 24 Info
• 3 points

COMPUTATIONAL ASPECTS OF ROBOTICS

COMPUTATNL ASPECTS OF ROB

Introduction to fundamental problems and algorithms in robotics. Topics include configuration spaces, motion and sensor models, search and sampling-based planning, state estimation, localization and mapping, perception, and learning.

• F 1:10 p.m.-3:40 p.m.
• Instructor: Tony B Dear
  CULPA: 17 Info
• 3 points

COMPUTATIONAL GENOMICS

Computational techniques for analyzing genomic data including DNA, RNA, protein and gene expression data. Basic concepts in molecular biology relevant to these analyses. Emphasis on techniques from artificial intelligence and machine learning. String-matching algorithms, dynamic programming, hidden Markov models, expectation-maximization, neural networks, clustering algorithms, support vector machines. Students with life sciences backgrounds who satisfy the prerequisites are encouraged to enroll.

• MW 5:40 p.m.-6:55 p.m.
• Instructor: Itsik Pe'er
  Wikipedia Info
• 3 points

MACHINE LEARNING

Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in MATLAB.

• TR 1:10 p.m.-2:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Nakul Verma
  CULPA: 10 h-index: 16 Info
• 3 points

Machine Learning Theory

Theoretical study of algorithms for machine learning and high-dimensional data analysis. Topics include high-dimensional probability, theory of generalization and statistical learning, online learning and optimization, spectral analysis

• TR 8:40 a.m.-9:55 a.m.
• Instructor: Daniel Hsu
  CULPA: 6 h-index: 44 Info
• 3 points

EMBEDDED SYSTEMS

Embedded system design and implementation combining hardware and software. I/O, interfacing, and peripherals. Weekly laboratory sessions and term project on design of a microprocessor-based embedded system including at least one custom peripheral. Knowledge of C programming and digital logic required.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Stephen A Edwards
  CULPA: 21 h-index: 35 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

GEOMETRIC DATA ANALYSIS

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• TR 8:40 a.m.-9:55 a.m.
• Instructor: Andrew J Blumberg
  h-index: 33 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

COMPETITIVE PROGRAMMING

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• MW 5:40 p.m.-6:55 p.m.
• Instructor: Yongwhan Lim
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

INTRO TO DATA VISUALIZATION

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Christian Swinehart
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

APPLIED COMPUTER VISION

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• TR 5:40 p.m.-6:55 p.m.
• Instructor: Austin D Reiter
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

EMPIRICAL METHODS DATA SC

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Michelle F Levine
  CULPA: 6 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

INNOVATION & DESIGN LAB

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• T 1:10 p.m.-3:40 p.m.
• Instructor: Gary Zamchick
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

ADV SYSTEMS PROGRAMMING

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• W 4:10 p.m.-6:40 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

CAUSAL INFERENCE FOR DATA SCI

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• T 7 p.m.-9:30 p.m.
• Instructor: Adam Kelleher
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

APPLIED MACHINE LEARNING

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• W 4:10 p.m.-6:40 p.m.
• Instructor: Vijay Pappu
  Info
• 3 points

ADVANCED DATABASE SYSTEMS

Continuation of (COMS W4111), covers the latest trends in both database research and industry. Programming projects in Python are required.

• T 12:10 p.m.-2 p.m.
• Instructor: Luis Gravano
  CULPA: 16 h-index: 56 Info
• 3 points

TOPICS IN SOFTWARE ENGINEERING

Topics in Software engineering arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes, it may be repeated for credit with advisor approval. Consult the department for section assignment.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Gail E Kaiser
  CULPA: 10 Info
• 3 points

Human-Computer Interaction

Human–computer interaction (HCI) studies (1) what computers are used for, (2) how people interact with computers, and (3) how either of those should change in the future. Topics include ubiquitous computing, mobile health, interaction techniques, social computing, mixed reality, accessibility, and ethics. Activities include readings, presentations, and discussions of research papers. Substantial HCI research project required.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Brian A Smith
  CULPA: 2 h-index: 5 Info
• 3 points

ADVANCED CRYPTOGRAPHY

A study of advanced cryptographic research topics such as: secure computation, zero knowledge, privacy, anonymity, cryptographic protocols. Concentration on theoretical foundations, rigorous approach, and provable security. Contents varies between offerings. May be repeated for credit.

• R 1:10 p.m.-3:40 p.m.
• 3 points

HARDWARE SECURITY

• M 7 p.m.-9:30 p.m.
• Instructor: Simha Sethumadhavan
  CULPA: 8 Info
• 3 points

COMPUTATIONAL IMAGING

Computational imaging uses a combination of novel imaging optics and a computational module to  produce new forms of visual information. Survey of the state-of-the-art in computational imaging.  Review of recent papers on omnidirectional and panoramic imaging, catadioptric imaging, high  dynamic range imaging, mosaicing and superresolution. Classes are seminars with the instructor,  guest speakers, and students presenting papers and discussing them.

• M 4:10 p.m.-6 p.m.
• Instructor: Shree K Nayar
  CULPA: 12 Wikipedia Info
• 3 points

EMBEDDED SCALABLE PLATFORMS

Inter-disciplinary graduate-level seminar on design and programming of embedded scalable platforms. Content varies between offerings to cover timely relevant issues and latest advances in system-on-chip design, embedded software programming, and electronic design automation. Requires substantial reading of research papers, class participation, and semester-long project.

• W 6:10 p.m.-8 p.m.
• Instructor: Luca Carloni
  CULPA: 2 Wikipedia h-index: 45 Info
• 3 points

TECH WRITING FOR CS AND ENGINRS

Topics to help CS/CE and EE graduate students’ communication skills. Emphasis on writing, presenting clear, concise proposals, journal articles, conference papers, theses, and technical presentations.  Credit may not be used to satisfy degree requirements.

• F 10:10 a.m.-noon
• Instructor: Janet Kayfetz
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

ADV SPOKEN LANGUAGE PROCESSING

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• T 4:10 p.m.-6 p.m.
• Instructor: Julia Hirschberg
  CULPA: 8 h-index: 77 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

FUND SPEECH RECOGNITION

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 7 p.m.-9:30 p.m.
• Instructor: Homayoon Beigi
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

GENERATIVE MODELS FR CODE

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• F 2:10 p.m.-4 p.m.
• Instructor: Baishakhi Ray
  CULPA: 3 h-index: 24 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

PROGRAM ANALYSIS APPROX

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 1:10 p.m.-3:40 p.m.
• Instructor: Suman Jana
  h-index: 33 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

UNCOND LOW BNDS DERANDMZN

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• T 1:10 p.m.-3:40 p.m.
• Instructor: Rocco Servedio
  CULPA: 15 h-index: 48 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

HIGH PERF MACH LEARNING

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 7 p.m.-9:30 p.m.
• Instructor: Kaoutar El Maghraoui
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

PRIVATE SYSTEMS

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 12:10 p.m.-2 p.m.
• Instructor: Roxana Geambasu
  CULPA: 2 Wikipedia h-index: 20 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

COMP ARCH MODEL SIM EVAL

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Jose E Moreira
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

APPL MACHINE LEARN CLOUD

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• R 4:10 p.m.-6:40 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

ADV TOPICS PROJ DEEP LEARNING

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• W 2:10 p.m.-4 p.m.
• Instructor: Peter Belhumeur
  CULPA: 6 h-index: 59 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

ECONOMICS CYBERSECURITY

Selected topics in computer science (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

• T 6:10 p.m.-8 p.m.
• 3 points