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 1:10 p.m.-2:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Chris Murphy
  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.

• MW 2:40 p.m.-3:55 p.m.
• 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 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 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: Chris Murphy
  Info
• 1 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.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Daniel Bauer
  CULPA: 17 h-index: 13 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 2:40 p.m.-3:55 p.m.
• MW 5:40 p.m.-6:55 p.m.
• Instructor: Paul S Blaer
  CULPA: 46 h-index: 8 Info
• 3 points

ADVANCED PROGRAMMING

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

• MW 4:10 p.m.-5:25 p.m.
• MW 5:40 p.m.-6:55 p.m.
• Instructor: Brian Borowski
  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 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 1:10 p.m.-2:25 p.m.
• MW 2:40 p.m.-3:55 p.m.
• 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.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Donald F Ferguson
  CULPA: 17 Wikipedia 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.

• M 7 p.m.-9:30 p.m.
• Instructor: Hubertus Franke
  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

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.

• T 7 p.m.-9:30 p.m.
• Instructor: Celeste S Layne
  CULPA: 2 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

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.

• F 10:10 a.m.-12:40 p.m.
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Eleni Drinea
  CULPA: 8 Info
• Instructor: Alexandr Andoni
  CULPA: 2 h-index: 34 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

INTRO TO QUANTUM COMPUTING

• TR 2:40 p.m.-3:55 p.m.
• Instructor: James L Bartusek
  Info
• 3 points

Internet Technology, Economics, and Poli

INTERNET TECHN ECON AND P

Technology, economic and policy aspects of the Internet. Summarizes how the Internet works technically, including protocols, standards, radio spectrum, global infrastructure and interconnection. Micro-economics with a focus on media and telecommunication economic concerns, including competition and monopolies, platforms, and behavioral economics. US constitution, freedom of speech, administrative procedures act and regulatory process, universal service, role of FCC. Not a substitute for CSEE4119. Suitable for non-majors.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Henning G Schulzrinne
  CULPA: 12 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.

• TR 4:10 p.m.-5:25 p.m.
• TR 6:40 p.m.-7:55 p.m.
• Instructor: Adam Lin
  Info
• 3 points

NATURAL LANGUAGE PROCESSING

Computational approaches to the analysis, understanding, and generation of natural language text at scale. Emphasis on machine learning techniques for NLP, including deep learning and large language models. Applications may include information extraction, sentiment analysis, question answering, summarization, machine translation, and conversational AI. Discussion of datasets, benchmarking and evaluation, interpretability, and ethical considerations.
Due to significant overlap in content, only one of COMS 4705 or Barnard COMS 3705BC may be taken for credit.

• TR 4:10 p.m.-5:25 p.m.
• F 1:10 p.m.-3:40 p.m.
• T 7 p.m.-9:30 p.m.
• Instructor: Zhou Yu
  h-index: 22 Info
• Instructor: Daniel Bauer
  CULPA: 17 h-index: 13 Info
• Instructor: Andrei A Simion
  Info
• 3 points

Ethical and Responsible AI

ETHIC AND RESPONSIBLE AI

Principles of Ethical Artificial Intelligence across technical and societal dimensions. Combines technical AI and machine learning implementations and ethical analysis. Students will learn to build, audit, and mitigate ethical risks in AI systems using tools like fairness libraries, explainability frameworks, and privacy-preserving techniques. Emphasizes coding, algorithmic critique, and real-world cases.
Topics include: foundations of AI ethics, fairness, interpretability, explainability,  accountability, privacy, robustness, alignment, safety, and societal benefit. 
Assessments include coding projects, bias auditing assignments, and ethical analysis papers.

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Ansaf Salleb-Aouissi
  CULPA: 21 h-index: 16 Info
• 3 points

MACHINE LEARNING FOR DATA SCI

• TR 1:10 p.m.-2:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Yining Liu
  Info
• 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 9:10 a.m.-11:40 a.m.
• Instructor: Aleksander Holynski
  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.
• 3 points

MACHINE LEARNING

Basic statistical principles and algorithmic paradigms of supervised machine learning.

Prerequisites: 
Multivariable calculus (e.g. MATH1201 or MATH1205 or APMA2000), linear algebra (e.g. COMS3251 or MATH2010 or MATH2015), probability (e.g. STAT1201 or STAT4001 or IEOR3658 or MATH2015), discrete math (COMS3203), and general mathematical maturity. Programming and algorithm analysis (e.g. COMS 3134). 
COMS 3770 optionally satisfies all math prerequisites for this course.

• 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

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

SCIENCE OF BLOCKCHAINS

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 8:40 a.m.-9:55 a.m.
• Instructor: Timothy A Roughgarden
  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.
• F 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.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Jae W Lee
  CULPA: 42 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

C++ DEEP DIVE FOR C PRGS

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: Jae W Lee
  CULPA: 42 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

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.

• M 7 p.m.-9:30 p.m.
• Instructor: Yongwhan Lim
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

MACHINE LEARNING SECURITY

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 12:10 p.m.-2 p.m.
• Instructor: Suman Jana
  h-index: 33 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

THRY ML INTERACT DECISION

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 1:10 p.m.-2:25 p.m.
• Instructor: Adam B Block
  Info
• 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.

• M 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: Spencer Luo
  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.

• W 1:10 p.m.-3:40 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 2:10 p.m.-4 p.m.
• Instructor: Tal Malkin
  CULPA: 18 Wikipedia h-index: 45 Info
• 3 points

HARDWARE SECURITY

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Simha Sethumadhavan
  CULPA: 8 Info
• 3 points

Advanced Spoken Language Processing

ADV SPOKEN LANG PROC

Applications of spoken language processing, including text-to-speech and dialogue systems. Analysis of speech and text, including entrainment, empathy, personality, emotion, humor, sarcasm, deception, trust, radicalization, and charisma.

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

Advanced Topics in Natural Language Proc

LLM INTERP AND ALIGNMENT

Selected topics in Natural Language Processing (advanced level). Content and prerequisites vary between sections and semesters. May be repeated for credit. Check the "topics courses" webpage on the department website for more information on each section.

• F 12:10 p.m.-2 p.m.
• Instructor: John Hewitt
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

TPCS GEOMETRY 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.

• M 4:10 p.m.-6 p.m.
• Instructor: Silvia Sellan
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

TPCS SCALABLE MATRIX COMP

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 12:10 p.m.-2 p.m.
• 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.

• M 7 p.m.-9:30 p.m.
• Instructor: Kaoutar El Maghraoui
  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

FINE GRAINED COMPLEXITY

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 2:10 p.m.-4 p.m.
• Instructor: Josh Alman
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

QUANTUM ERROR CORRECTION

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 10:10 a.m.-noon
• Instructor: Henry Yuen
  CULPA: 1 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

READINGS COMP ARCH

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 10:10 a.m.-noon
• Instructor: Martha A Kim
  CULPA: 40 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

DEEP LEARNING ROBOT MANIP

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 10:10 a.m.-noon
• Instructor: Yunzhu Li
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

COMMUNICATION COMPLEXITY APPS

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 1:10 p.m.-3:55 p.m.
• Instructor: Toniann Pitassi
  Wikipedia Info
• 3 points

TOPICS IN COMPUTER SCIENCE

ROBUST CONTROLLABLE ML

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: Richard Zemel
  Wikipedia Info
• 3 points

SEMINAR IN COMPUTER SCIENCE

FRONTIERS CS RESEARCH

• T 2:40 p.m.-3:55 p.m.
• 3 points