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 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.
• Instructor: Chris Murphy
  Info
• 3 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 10:10 a.m.-12:40 p.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

ESSENTIAL DATA STRUCTURES

A second programming course intended for nonmajors with at least one semester of introductory programming experience. Basic elements of programming in C and C++, arraybased data structures, heaps, linked lists, C programming in UNIX environment, object-oriented programming in C++, trees, graphs, generic programming, hash tables. Due to significant overlap, students may only receive credit for either COMS W3134, W3136, or W3137.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Brian Borowski
  Info
• 4 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 8:40 a.m.-9:55 a.m.
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 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: Josh Alman
  Info
• 3 points

Mathematics for Machine Learning

MATH FOR MACHINE LEARNING

Mathematical foundations of machine learning: Linear algebra, multivariable calculus, and probability and statistics. Comprehensive review and additional treatment of
relevant topics used in the analysis and design of machine learning models. Preliminary exposure to core algorithms such as linear regression, gradient descent, principal
component analysis, low-rank approximations, and kernel methods.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Tony B Dear
  CULPA: 17 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 8:40 a.m.-9:55 a.m.
• TR 4:10 p.m.-5:25 p.m.
• Instructor: Zishen Wan
  Info
• Instructor: Stephen A Edwards
  CULPA: 21 h-index: 35 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.

• TR 11:40 a.m.-12:55 p.m.
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Luis Gravano
  CULPA: 16 h-index: 56 Info
• Instructor: Eugene Wu
  CULPA: 1 h-index: 32 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

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.

• M 6:10 p.m.-8 p.m.
• Instructor: Ricardo Miotto
  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 4:10 p.m.-5:25 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

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.
• TR 8:40 a.m.-9:55 a.m.
• Instructor: Eleni Drinea
  CULPA: 8 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 CRYPTOGRAPHY

An introduction to modern cryptography, focusing on the complexity-theoretic foundations of secure computation and communication in adversarial environments; a rigorous approach, based on precise definitions and provably secure protocols. Topics include private and public key encryption schemes, digital signatures, authentication, pseudorandom generators and functions, one-way functions, trapdoor functions, number theory and computational hardness, identification and zero knowledge protocols.

• 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.

• MW 4:10 p.m.-5:25 p.m.
• MW 5:40 p.m.-6:55 p.m.
• Instructor: Tony B Dear
  CULPA: 17 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.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Daniel Bauer
  CULPA: 17 h-index: 13 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 11:40 a.m.-12:55 p.m.
• 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

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.

• TR 5:40 p.m.-6:55 p.m.
• Instructor: Itsik Pe'er
  Wikipedia Info
• 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 is recommended for students who wish to refresh their math background.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Adam B Block
  Info
• 3 points

Unsupervised Learning

Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of datastructures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python.

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

EMBEDDED SYSTEMS

• 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

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.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Suman Jana
  h-index: 33 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

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 2:40 p.m.-3:55 p.m.
• Instructor: Kostis Kaffes
  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

STRT STUDIO AI ACCEL BLDG

• F 1:10 p.m.-3:40 p.m.
• Instructor: Ken St. Clair
  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.

• W 5 p.m.-7:30 p.m.
• Instructor: Adam Kelleher
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

APPLIED DEEP 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.

• T 7 p.m.-9:30 p.m.
• Instructor: Andrei A Simion
  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.

• MW 1:10 p.m.-2:25 p.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 4:10 p.m.-6:40 p.m.
• Instructor: Brian A Smith
  CULPA: 2 h-index: 5 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

EMBEDDED SCALABLE PLATFORMS

• 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

AI AGENTS CAPABL RELIABL

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.

• M 4:10 p.m.-6 p.m.
• Instructor: Zhou Yu
  h-index: 22 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

ENGR WEB3 BLOCKCHAIN 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 4:10 p.m.-6 p.m.
• Instructor: Ronghui Gu
  h-index: 11 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

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 8:10 a.m.-10 a.m.
• Instructor: Roxana Geambasu
  CULPA: 2 Wikipedia h-index: 20 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

CAUSAL AI AND LLMS

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.-12:40 p.m.
• Instructor: Elias Bareinboim
  h-index: 25 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

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: Henry Yuen
  CULPA: 1 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.

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

TOPICS IN COMPUTER SCIENCE

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: Baishakhi Ray
  CULPA: 3 h-index: 24 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 10:10 a.m.-noon
• Instructor: Vasileios Kalantzis
  Info
• 3 points

SEMINAR IN COMPUTER SCIENCE

FRONTIERS CS RESEARCH

• T 2:40 p.m.-3:55 p.m.
• 3 points