COMPUTING IN CONTEXT

CONTEXTS: SOC SCI

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002.

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 4 points

COMPUTING IN CONTEXT

COMPUTING IN LINGUISTICS

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002.

• TR 1:10 p.m.-2:25 p.m.
• 4 points

COMPUTING IN CONTEXT

COMPUTING IN ECONOMICS

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 4 points

COMPUTING IN CONTEXT

COMPUTING IN BIOLOGY

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002.

• TR 2:40 p.m.-3:55 p.m.
• 4 points

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.

• 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

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: Daniel Bauer
  CULPA: 17 h-index: 13 Info
• 3 points

COMPUTING IN CONTEXT REC

COMPUTING IN ECON

• R 7:10 p.m.-8 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 points

COMPUTING IN CONTEXT REC

COMPUTING IN ECON

• R 7:10 p.m.-8 p.m.
• F 10:10 a.m.-11 a.m.
• F 2 p.m.-2:50 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 points

COMPUTING IN CONTEXT REC

COMPUTING IN SOC SCI

• R 7:10 p.m.-8 p.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 points

COMPUTING IN CONTEXT REC

COMPUTING IN SOC SCI

• R 7:10 p.m.-8 p.m.
• F 9 a.m.-9:50 a.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 points

COMPUTING IN CONTEXT REC

COMPUTATIONAL LINGUISTICS

• R 7:10 p.m.-8 p.m.
• F 10:10 a.m.-11 a.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 points

COMPUTING IN CONTEXT REC

COMPUTING IN BIOLOGY

• R 7:10 p.m.-8 p.m.
• F 11 a.m.-11:50 a.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 0 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

AI in Context

AI IN CONTEXT

An interdisciplinary introduction to the history, development and modern application of artificial intelligence in a variety of contexts. Context subjects and teaching staff will vary by semester.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Adam H Cannon
  CULPA: 81 Info
• 3 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

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.

• TR 5:40 p.m.-6:55 p.m.
• Instructor: Timothy K Paine
  CULPA: 4 Info
• 4 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.
• F 12:10 p.m.-2 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).

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

• TR 8:40 a.m.-9:55 a.m.
• TR 10:10 a.m.-11:25 a.m.
• Instructor: Tal Malkin
  CULPA: 18 Wikipedia h-index: 45 Info
• 3 points

COMPUTERS AND SOCIETY

Broader impact of computers. Social networks and privacy. Employment, intellectual property, and the media. Science and engineering ethics. Suitable for nonmajors.

• W 4:10 p.m.-6:40 p.m.
• Instructor: Ronald Baecker
  Wikipedia 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 11:40 a.m.-12:55 p.m.
• TR 1:10 p.m.-2:25 p.m.
• 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 10:10 a.m.-11:25 a.m.
• TR 8:40 a.m.-9:55 a.m.
• F 10:10 a.m.-12:40 p.m.
• Instructor: Luis Gravano
  CULPA: 16 h-index: 56 Info
• Instructor: Eugene Wu
  CULPA: 1 h-index: 32 Info
• Instructor: Donald F Ferguson
  CULPA: 17 Wikipedia Info
• 3 points

FUND-LARGE-SCALE DIST SYSTEMS

Prerequisites: (COMS W3134 or COMS W3136 or COMS W3137) and (COMS W3157 or COMS W4118 or CSEE W4119) Design and implementation of large-scale distributed and cloud systems. Teaches abstractions, design and implementation techniques that enable the building of fast, scalable, fault-tolerant distributed systems. Topics include distributed communication models (e.g. sockets, remote procedure calls, distributed shared memory), distributed synchronization (clock synchronization, logical clocks, distributed mutex), distributed file systems, replication, consistency models, fault tolerance, distributed transactions, agreement and commitment, Paxos-based consensus, MapReduce infrastructures, scalable distributed databases. Combines concepts and algorithms with descriptions of real-world implementations at Google, Facebook, Yahoo, Microsoft, LinkedIn, etc.

• F 10:10 a.m.-12:40 p.m.
• Instructor: Roxana Geambasu
  CULPA: 2 Wikipedia h-index: 20 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.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Baishakhi Ray
  CULPA: 3 h-index: 24 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: Jason Nieh
  CULPA: 23 Wikipedia h-index: 50 Info
• 3 points

Cloud Computing

CLOUD COMPUTING

Software engineering skills necessary for developing cloud computing and software-as-a-service applications, covering topics such as service-oriented architectures, message-driven applications, and platform integration. Includes theoretical study, practical application, and collaborative project work.

• F 1:10 p.m.-3:40 p.m.
• Instructor: Donald F Ferguson
  CULPA: 17 Wikipedia Info
• 3 points

ADVANCED SOFTWARE ENGINEERING

Software lifecycle using frameworks, libraries and services. Major emphasis on software testing. Centers on a team project.

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

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Brian A Smith
  CULPA: 2 h-index: 5 Info
• 3 points

SECURITY I

Introduction to security. Threat models. Operating system security features. Vulnerabilities and tools. Firewalls, virtual private networks, viruses. Mobile and app security. Usable security. Note: May not earn credit for both W4181 and W4180 or W4187.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Suman Jana
  h-index: 33 Info
• 3 points

MALWARE ANALYSIS&REVERSE ENGINEERING

Hands-on analysis of malware. How hackers package and hide malware and viruses to evade analysis. Disassemblers, debuggers, and other tools for reverse engineering. Deep study of Windows Internals and x86 assembly.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Michael S Sikorski
  CULPA: 2 Info
• 3 points

Data-driven Decision Modeling

Introduction to modeling, estimating, and solving decision-making problems in the context of artificial intelligence and analytics. Potential topics include choice models, quantity models, online learning using multi-armed bandits, dynamic decision modeling, dynamic games, and Bayesian learning theory. Practice both theory and applications using Python programming.

• MW 2:40 p.m.-3:55 p.m.
• Instructor: Tony B Dear
  CULPA: 17 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.

• MW 2:40 p.m.-3:55 p.m.
• MW 1:10 p.m.-2:25 p.m.
• Instructor: Alexandr Andoni
  CULPA: 2 h-index: 34 Info
• Instructor: Mihalis Yannakakis
  CULPA: 14 Wikipedia h-index: 93 Info
• 3 points

INTRO-COMPUTATIONAL COMPLEXITY

Develops a quantitative theory of the computational difficulty of problems in terms of the resources (e.g. time, space) needed to solve them. Classification of problems into complexity classes, reductions, and completeness. Power and limitations of different modes of computation such as nondeterminism, randomization, interaction, and parallelism.

• MW 8:40 a.m.-9:55 a.m.
• Instructor: Xi Chen
  CULPA: 7 Wikipedia h-index: 64 Info
• 3 points

ALGORITHMS FOR DATA SCIENCE

Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

• TR 11:40 a.m.-12:55 p.m.
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Eleni Drinea
  CULPA: 8 Info
• 3 points

INTRO TO QUANTUM COMPUTING

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Henry Yuen
  CULPA: 1 Info
• 3 points

PROGRAMMING & PROBLEM SOLVING

Hands-on introduction to solving open-ended computational problems. Emphasis on creativity, cooperation, and collaboration. Projects spanning a variety of areas within computer science, typically requiring the development of computer programs. Generalization of solutions to broader problems, and specialization of complex problems to make them manageable. Team-oriented projects, student presentations, and in-class participation required.

• MW 1:10 p.m.-2:25 p.m.
• Instructor: Kenneth A Ross
  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.

• F 10:10 a.m.-12:40 p.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 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
• 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.

• F 10:10 a.m.-12:40 p.m.
• MW 4:10 p.m.-5:25 p.m.
• Instructor: Daniel Bauer
  CULPA: 17 h-index: 13 Info
• Instructor: Zhou Yu
  h-index: 22 Info
• 3 points

Computer Vision I: First Principles

Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications.

• MW 10:10 a.m.-11:25 a.m.
• Instructor: Shree K Nayar
  CULPA: 12 Wikipedia Info
• 3 points

Machine Learning for Functional Genomics

ML for Functional Genomic

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used  to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins.

• F 1:10 p.m.-3:40 p.m.
• Instructor: David A Knowles
  h-index: 35 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 2:40 p.m.-3:55 p.m.
• Instructor: Nakul Verma
  CULPA: 10 h-index: 16 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

Causal Inference

Causal Inference theory and applications. The theoretical topics include the 3-layer causal hierarchy,  causal bayesian networks, structural learning, the identification problem and the do-calculus, linear identifiability, bounding, and counterfactual analysis. The applied part includes intersection with statistics, the empirical-data sciences (social and health), and AI and ML.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Elias Bareinboim
  h-index: 25 Info
• 3 points

COMPUTER ARCHITECTURE

Focuses on advanced topics in computer architecture, illustrated by case studies from classic and modern processors. Fundamentals of quantitative analysis. Pipelining. Memory hierarchy design. Instruction-level and thread-level parallelism. Data-level parallelism and graphics processing units. Multiprocessors. Cache coherence. Interconnection networks. Multi-core processors and systems-on-chip. Platform architectures for embedded, mobile, and cloud computing.

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

SYSTEM-ON-CHIP PLATFORMS

Design and programming of System-on-Chip (SoC) platforms. Topics include: overview of technology and economic trends, methodologies and supporting CAD tools for system-level design, models of computation, the SystemC language, transaction-level modeling, software simulation and virtual platforms, hardware-software partitioning, high-level synthesis, system programming and device drivers, on-chip communication, memory organization, power management and optimization, integration of programmable processor cores and specialized accelerators. Case studies of modern SoC platforms for various classes of applications.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Luca Carloni
  CULPA: 2 Wikipedia h-index: 45 Info
• 3 points

PROJECTS IN COMPUTER SCIENCE

A second-level independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit. Consult the department for section assignment.

• Instructor: Julia Hirschberg
  CULPA: 8 h-index: 77 Info
• 1-15 points

TOPICS IN COMPUTER SCIENCE

HACKING 4 DEFENSE

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 4:10 p.m.-6:40 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

DESIGN USING C++

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: Bjarne Stroustrup
  CULPA: 1 Wikipedia h-index: 38 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

PARALLEL FUNCTIONAL PROGRAMMIN

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

DEEP LRNG FOR COMP 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 2:40 p.m.-3:55 p.m.
• Instructor: Peter Belhumeur
  CULPA: 6 h-index: 59 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

LAB COMPUTATIONAL BIOLOGY

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: Itsik Pe'er
  Wikipedia 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.

• TR 5:40 p.m.-6:55 p.m.
• Instructor: Yongwhan Lim
  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

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.

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

TOPICS IN COMPUTER SCIENCE

MATH MACH LEARN SIG RECOG

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: Homayoon Beigi
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

MOBILE APP DEVELOP IOS

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 4:10 p.m.-5:25 p.m.
• Instructor: Hugh Thomas
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

Randomized Algorithms

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

TOPICS IN COMPUTER SCIENCE

ELEMENTS FOR DATA 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.

• M 7 p.m.-9:30 p.m.
• Instructor: Andi Cupallari
  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.

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

• T 4:10 p.m.-6:40 p.m.
• R 7 p.m.-9:30 p.m.
• Instructor: Vijay Pappu
  Info
• 3 points

Virtual Reality and Augmented Reality

Virtual and Augmented Rea

This course will cover selected topics in virtual reality (VR) and augmented reality (AR). There are two main components, with everyone participating in both: papers and projects.

• W 2:10 p.m.-4 p.m.
• Instructor: Steven K Feiner
  CULPA: 18 Wikipedia Info
• 3 points

MODELING & PERFORMANCE EVALUATION

Introduction to queuing analysis and simulation techniques. Evaluation of time-sharing and multiprocessor systems. Topics include priority queuing, buffer storage, and disk access, interference and bus contention problems, and modeling of program behaviors.

• T 10:10 a.m.-noon
• Instructor: Vishal Misra
  CULPA: 5 Wikipedia h-index: 53 Info
• 3 points

FORMAL VERIF HW SW SYSTEMS

Introduction to the theory and practice of formal methods for the design and analysis of correct (i.e. bug-free) concurrent and embedded hardware/software systems. Topics include temporal logics; model checking; deadlock and liveness issues; fairness; satisfiability (SAT) checkers; binary decision diagrams (BDDs); abstraction techniques; introduction to commercial formal verification tools. Industrial state-of-art, case studies and experiences: software analysis (C/C++/Java), hardware verification (RTL).

• M 6:10 p.m.-8 p.m.
• 3 points

PROJECTS IN COMPUTER SCIENCE

Software or hardware projects in computer science. Before registering, the student must submit a written proposal to the instructor for review. The proposal should give a brief outline of the project, estimated schedule of completion, and computer resources needed. Oral and written reports are required. May be taken over more than one semester, in which case the grade will be deferred until all 12 points have been completed. No more than 12 points of COMS E6901 may be taken. Consult the department for section assignment.

• Instructor: Eleni Drinea
  CULPA: 8 Info
• Instructor: Julia Hirschberg
  CULPA: 8 h-index: 77 Info
• 1-15 points

TOPICS IN COMPUTER SCIENCE

MACHINE LEARNING FRONTRS

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 2:10 p.m.-4 p.m.
• Instructor: Carl Vondrick
  CULPA: 1 h-index: 24 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

READINGS LANGUAGE DESIGN

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 10:10 a.m.-noon
• Instructor: Bjarne Stroustrup
  CULPA: 1 Wikipedia h-index: 38 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

TOPICS CLOUD COMPUTING

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: Kostis Kaffes
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

ADV TPCS COMPETITIVE PROG

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 5:40 p.m.-6:55 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

TOPICS MOBILE COMPUTING

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.
• Instructor: Xia Zhou
  Info
• 3 points

TOPICS IN COMPUTER SCIENCE

GRAPHICS & COMP MOTION

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: Changxi Zheng
  h-index: 30 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