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 10:10 a.m.-1:20 p.m.
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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.
- MW 5:30 p.m.-8:40 p.m.
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Instructor:
Paul S Blaer
CULPA: 46
h-index: 8
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.
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 5:30 p.m.-8:40 p.m.
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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.
- TR 5:30 p.m.-8:40 p.m.
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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 10:10 a.m.-1:20 p.m.
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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 p.m.-4:10 p.m.
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Instructor:
Xi Chen
CULPA: 7
Wikipedia
h-index: 64
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 5:30 p.m.-8:40 p.m.
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Instructor:
Timothy K Paine
CULPA: 4 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 1 p.m.-4:10 p.m.
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Instructor:
Nakul Verma
CULPA: 10
h-index: 16
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.
- MW 10:10 a.m.-1:20 p.m.
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Instructor:
Periklis Papakonstantinou
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.-7:20 p.m.
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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 4:10 p.m.-7:20 p.m.
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Instructor:
Daniel Bauer
CULPA: 17
h-index: 13
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 p.m.-4:10 p.m.
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Instructor:
Nakul Verma
CULPA: 10
h-index: 16
Info
- 3 points
TOPICS IN COMPUTER SCIENCE
ALGORITHM TO DEVEL
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 5:30 p.m.-8:40 p.m.
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Instructor:
Brian Borowski
Info - 3 points
TOPICS IN COMPUTER SCIENCE
MATH FOR ML
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 10:10 a.m.-1:20 p.m.
- 3 points