Summer Sessions | Courses | Computer Science

Computer Science

The Computer Science Department offers an integrated curriculum during the summer term. Major course topics include programming languages, artificial intelligence, natural language processing, computational complexity, and the analysis of algorithms.

The courses on this page reflect Summer 2018 offerings. 

 

Courses
Expand All
Analysis of Algorithms
CSOR S4231D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
CSOR 4231 001/66892 Tu Th 1:00p - 4:10p
524 SEELEY W. MUDD BUILDING
Eleni Drinea 3 Open
Artificial Intelligence
COMS S4701D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 4701 001/65676 Tu Th 1:00p - 4:10p
545 SEELEY W. MUDD BUILDING
Ansaf Salleb-Aouissi 3 Open
Computer Architecture
CSEE S4824D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
CSEE 4824 001/66424 Tu Th 5:30p - 8:40p
337 SEELEY W. MUDD BUILDING
Luca Carloni 3 Open
Computer Networks
CSEE S4119D 3 points.

Pre or Corequisites: Calculus based Probability and Statistics. 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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
CSEE 4119 001/75804 Tu Th 5:30p - 8:40p
1127 SEELEY W. MUDD BUILDING
Gil Zussman 3 Open
Computer Science Theory
COMS S3261D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 3261 001/74588 Tu Th 1:00p - 4:10p
227 SEELEY W. MUDD BUILDING
Xi Chen 3 Open
Discrete Mathematics: Introduction to Combinatorics and Graph Theory
COMS S3203D 3 points.

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).

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 3203 001/23551 M W 5:30p - 8:40p
633 SEELEY W. MUDD BUILDING
Robert Holliday 3 Open
Introduction to Computer Science and Programming in Java
COMS S1004D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 1004 001/15855 M W 5:30p - 8:40p
614 SCHERMERHORN HALL
Paul Blaer 3 Open
Machine Learning
COMS S4771D 3 points.

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.    

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 4771 001/14639 M W 5:30p - 8:40p
603 HAMILTON HALL
German Creamer 3 Open
Topics in Computer Science: Methods in Unsupervised Learning
COMS S4995D 3 points.

Prerequisites: Machine Learning (COMS 4771) or equivalent. Topics from unsupervised learning such as clustering and dimensionality reduction 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 of datastructures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python. 

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 4995 001/14172 Tu Th 1:00p - 4:10p
627 SEELEY W. MUDD BUILDING
Nakul Verma 3 Open
Computer Science Theory
COMS S3261Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 3261 002/63779 M W 5:30p - 8:40p
413 INTERNATIONAL AFFAIRS BLDG
Robert Holliday 3 Open
Data Structures in JAVA
COMS S3134Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 3134 001/68108 M W 5:30p - 8:40p
633 SEELEY W. MUDD BUILDING
Paul Blaer 3 Open
Fundamentals of Computer Systems
CSEE S3827Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
CSEE 3827 001/22335 Tu Th 5:30p - 8:40p
313 FAYERWEATHER
Timothy Paine 3 Open
Introduction to Computing for Engineers and Applied Scientists
ENGI S1006Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
ENGI 1006 001/24767 Tu Th 5:30p - 8:40p
633 SEELEY W. MUDD BUILDING
Daniel Bauer 3 Open
Machine Learning
COMS S4771Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
COMS 4771 002/75336 Tu Th 1:00p - 4:10p
417 MATHEMATICS BUILDING
Nakul Verma 3 Open