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Senior Level Courses

Top ⇑CMPS 4010 – Capstone Project Part 1 of 2

Course Description

This is the first semester of a two-semester course devoted to the development of the student's capstone project, which is required for the Computer Science coordinate major. Each student is overseen by a faculty advisor in computer science, in coordination with a faculty advisor from the area in which the project aims to demonstrate the application of computer science to that discipline. No credit is given for this course alone; credit of 3 hours is given for the combined courses CMPS 4010 and 4020.

Prerequisites

Approval of the department.


Top ⇑CMPS 4020 – Capstone Project Part 2 of 2

Course Description

This is the second of a two-semester course devoted to the development of the student's capstone project, which is required for the Computer Science coordinate major. Each student is overseen by a faculty advisor in computer science, in coordination with a faculty advisor from the area in which the project aims to demonstrate the application of computer science to that discipline. The credit for the combined CMPS 4010 and CMPS 4020 sequence is 3 hours.

Prerequisites

Approval of the department.


Top ⇑CMPS 4150/6150 – Advanced Topics of Artificial Intelligence

Course Description

This course will cover advanced topics in artificial intelligence. The topic will be announced at the start of each semester.

Prerequisites

Approval of the department.


Top ⇑CMPS 4230/6230 – Advanced Computational Geometry

Course Description

This course covers a selection of advanced geometric algorithms and geometric data structures, and their application to other disciplines. Selected topics may include: Dynamic and kinetic data structures, geometric algorithms and data structures in higher dimensions, shape analysis and matching, robustness and implementation issues, geometric approximation algorithms. Applications to disciplines such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems (GIS), VLSI, robotics, computer graphics, and geometric modeling will be discussed.

Prerequisites

CMPS 3130/6130 or permission of the instructor.


Top ⇑CMPS 4250/6250 – Mathematical Foundations of Computer Security

Course Description

This course studies the mathematics underlying computer security, including both public key and symmetric key cryptography, crypto-protocols and information flow. The course includes a study of the RSA encryption scheme, stream and clock ciphers, digital signatures and authentication. It also considers semantic security and analysis of secure information flow. (Same as MATH 4250.)

Prerequisites

One semester of Calculus, CMPS/MATH 2170, and permission of instructor.


Top ⇑CMPS 4610 – Algorithms

Course Description

This course covers fundamental algorithm design principles and data structures, basic notions of complexity theory, as well as an advanced introduction to parallel algorithms, randomized algorithms, and approximation algorithms. Topics include: divide-and-conquer, dynamic programming, amortized analysis, graph algorithms, network flow, map reduce, and more advanced topics in approximation algorithms and randomized algorithms.

Prerequisites

CMPS 2200 or equivalent and CMPS/MATH 2170 or equivalent, or permission by the instructor.


Top ⇑CMPS 4620 – Artificial Intelligence

Course Description

This course is designed for graduate students interested in understanding the design of autonomous intelligent agents. The course will cover fundamental notions and concepts such as uninformed and informed search, local search, constraint satisfaction and constraint-based optimization, Bayesian Networks, Markov Decision Problems and a short introduction on machine learning. Furthermore, advance topics and applications in the context of natural language processing, reasoning about time, algorithmic game theory and computational social choice will be considered as well.

Prerequisites

CMPS 2200 or equivalent or instructor approval.


Top ⇑CMPS 4630 – Computational Biology and Bioinformatics

Course Description

This course is an introduction to computational methods in molecular biology. Topics covered include: sequence analysis and alignment, sequencing technologies, genome and metagenomic sequencing, protein structure and structure prediction, and phylogenetic analysis. No prior background in biology is assumed.

Prerequisites

Undergraduate Algorithms course or equivalent and/or CMPS/MATH 2170 or equivalent or instructor approval


Top ⇑CMPS 4640 – Computational Geometry

Course Description

This course covers fundamental and advanced principles for designing and analyzing geometric algorithms and data structures, and their application to other disciplines. Computational Geometry is a young discipline which enjoys close relations to mathematics and to various application areas such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems, VLSI, robotics, computer graphics and geometric modeling. Selected topics may include: Dynamic and kinetic data structures, geometric algorithms and data structures in two and higher dimensions, shape analysis and matching, robustness and implementation issues, geometric approximation algorithms.

Prerequisites

CMPS 2200 or equivalent, or permission by the instructor. CMPS 3130 or undergraduate Algorithms preferred.


Top ⇑CMPS 4710 – Computational Complexity

Course Description

This course is an advanced introduction to the area of computational complexity. Topics covered include: impossibility and separability results for classical computation, interactive theorem proving and the PCP theorem, derandomization and hardness of approximation, and the quantum model of computation.

Prerequisites

Advanced undergraduate Algorithms course, graduate Algorithms course, and/or CMPS 3250, or instructor approval.


Top ⇑CMPS 4720 – Machine Learning

Course Description

This course will cover fundamental and advanced topics in machine learning. Topics will include linear and logistic regression, Lasso, preceptrons, deep neural networks, support vector machines, kernel methods, graphical models, principal and independent component analysis and Gaussian processes. In addition to thoroughly addressing theoretical aspects, several examples will illustrate the application of the different techniques.

Prerequisites

CMPS 2200 or equivalent and CMPS/MATH 2170 or equivalent or instructor approval.


Top ⇑CMPS 4910/6910 – Independent Study in Computer Science

Course Description

This is a directed study course that allows a student to pursue a topic of particular interest under the direction of a computer science faculty member. No more than three hours of 4910-4920 may be counted toward satisfying the major requirements.

Prerequisites

Approval of the department.


Top ⇑CMPS 4920 – Independent Study in Computer Science

Course Description

This is a directed study course that allows a student to pursue a topic of particular interest under the direction of a computer science faculty member. No more than three hours of 4910-4920 may be counted toward satisfying the major requirements.

Prerequisites

Approval of the department.


Top ⇑CMPS 4990/5000 – Honors Thesis in Computer Science

Course Description

This course is for students completing an honors thesis in computer science.

Prerequisites

Approval of the department.


Top ⇑CMPS 7980 – Independent Study in Computer Science

Course Description

This is a directed study course that allows a graduate student to pursue a topic of particular interest under the direction of a computer science faculty member.

Prerequisites

Approval of the department.

School of Science and Engineering, 201 Lindy Boggs Center, New Orleans, LA 70118 504-865-5764 sse@tulane.edu