INTRO-COMPUT SCI/PROG IN JAVA

INTRO-COMPUTER PROGRAM 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 5:30 p.m.-8:40 p.m.
• Instructor: Paul S Blaer
  CULPA: 46 h-index: 8 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 5:30 p.m.-8:40 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.

• TR 5:30 p.m.-8:40 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 10:10 a.m.-1:20 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 10:10 a.m.-1:20 p.m.
• Instructor: Xi Chen
  CULPA: 7 Wikipedia h-index: 64 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.

• TR 5:30 p.m.-8:40 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

IMPLEMNTING ESSENTIAL ALG

• MW 5:30 p.m.-8:40 p.m.
• Instructor: Brian Borowski
  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.
• Instructor: Nakul Verma
  CULPA: 10 h-index: 16 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.-7:20 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.

• TR 4:10 p.m.-7:20 p.m.
• 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.
• Instructor: Nakul Verma
  CULPA: 10 h-index: 16 Info
• 3 points

TOPICS IN COMPUTER SCIENCE

APPLIED CRYPTOGRAPHY

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.
• 3 points

TOPICS IN COMPUTER SCIENCE

SEMANTIC REPRESENTATIONS NLP

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 5:30 p.m.-8:40 p.m.
• 3 points

TOPICS IN COMPUTER SCIENCE

ETHICAL AI

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.
• Instructor: Ansaf Salleb-Aouissi
  CULPA: 21 h-index: 16 Info
• 3 points

FIELDWORK

FIELDWORK S

Final report required. This course may not be taken for pass/fail credit or audited.

• Instructor: Eleni Drinea
  CULPA: 8 Info
• 1 points