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Junior Level Courses

Computer Science Courses

Here is a list of the junior level courses currently available in computer science. Not every course is offered every year - the department rotates the topics so that each area is represented at least once every two years.

Top ⇑CMPS 3110 – Introduction to Computational Biology and Bioinformatics

Course Description

This course gives an overview of numerous fundamental areas in computational biology: computational sequence analysis, sequencing technologies and algorithms, protein structure prediction and determination, systems biology and phylogenetic analysis. These areas are covered with a focus on understanding why and how engineering and computational methods are applied to real-world biological questions.

Prerequisites

CMPS 1600, CMPS 2200.


Top ⇑CMPS 3120/6120 – Special Topics

Course Description

This course varies from time to time, focusing on topics of interest to the faculty and students.

Prerequisites

Permission of the instructor.


Top ⇑CMPS 3130/6130 – Introduction to Computational Geometry

Course Description

This course provides an introduction to geometric algorithms and geometric data structures. 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 (GIS), VLSI, robotics, computer graphics and geometric modeling. Covered topics include fundamental geometric algorithm design and analysis paradigms, geometric data structures for planar subdivisions and range searching, algorithms to computer the convex hull, Voronoi diagrams, and Delaunay triangulation, as well as selected advanced topics.

Prerequisites

CMPS 2200 or permission of the instructor.


Top ⇑CMPS 3140/6140 – Introduction to Artificial Intelligence

Course Description

The aim of this course is to provide the student with an introduction to the main concepts and techniques playing a key role in the modern arena of artificial intelligence. In addition to covering the main topics that concern modern AI, particular attention will be devoted to its applications in several fields. Among the topics covered are, “What is an intelligent artificial agent?”, problem solving using search and constraint satisfaction, uncertainty, Bayesian networks and probabilistic inference, supervised learning, planning, sequential decision problems, as well as several additional topics.

Prerequisites

CMPS 1500, CMPS/MATH 2170.


Top ⇑CMPS 3210/6210 – Algorithms for Computational Structural Biology

Course Description

Over the last few decades, as we have been able to determine whole genome sequences, structural biologists have sought to determine and catalog protein structures with an increasing reliance on computational methods. Automated methods to analyze protein structure make it possible to leverage information from previously solved structures, and to interpret experimental data in a principled way. In this course, we will focus on the myriad of algorithms for analyzing numerous aspects of protein structure and protein-protein interactions.

Prerequisites

CMPS 1600, CMPS 2200.


Top ⇑CMPS 3240/6240 – Machine Learning

Course Description

This course provides an introduction to the fundamental concepts of machine learning and statistical pattern recognition. In addition, several examples of applications will be described. The topics covered include generative/discriminative and parametric/non-parametric supervised learning, including neural networks; unsupervised learning, including clustering, dimensionality reduction and kernel methods; learning theory, including tradeoffs, large margins and VC theory; reinforcement learning, including criteria for optimality, brute force methods, value function methods and direct policy search; feedforward/feedback adaptive control, direct/indirect adaptive control methods; and various applications.

Prerequisites

CMPS 1500, CMPS/MATH 2170.


Top ⇑CMPS 3250 – Theory of Computation

Course Description

This course is an introduction to the theory of computation. It begins with regular languages and their representation as finite state automata, and continues with context free languages and pushdown automata. Turing machines and the Church-Turing Thesis are also considered, as well as decidability and reducibility. The basic notions of complexity theory area also covered, including P and NP for time complexity, as well as basic results about space complexity. (Same as MATH 3250.)

Prerequisite

CMPS/MATH 2170 or equivalent.


Top ⇑CMPS 3260 – Algorithms and Complexity

Course Description

This course is an introduction to the theory of computation. It begins with regular languages and their representation as finite state automata, and continues with context free languages and pushdown automata. Turing machines and the Church-Turing Thesis are also considered, as well as decidability and reducibility. The basic notions of complexity theory area also covered, including P and NP for time complexity, as well as basic results about space complexity. (Same as MATH 3260.)

Prerequisite

CMPS/MATH 2170 or equivalent.


Top ⇑CMPS 3280/6280 – Information Theory

Course Description

This course is an introduction to Shannon’s mathematical theory of information. It considers basic concepts such as information content, entropy and the Kullback-Leibler distance, as well as areas such as data compression and Shannon’s Source Coding Theorem, coding, prefix codes, lossless channels and their capacity, and Shannon’s Noisy Coding Theorem. Applications to various areas are also featured in the course. (Same as MATH 3280.)

Prerequisite

MATH 3050 or MATH 3090 and familiarity with discrete probability or permission of instructor.


Top ⇑CMPS 3310/6310 – Logic in Computer Science

Course Description

This course is an introduction to logic and its applications in computer science. The topics covered include soundness and completeness of propositional logic, predicate logic, linear time temporal logic and branching time temporal logics and their expressive power, frameworks for software verification, Hoare triples, partial and total correctness, modal logics and agents, and binary decision diagrams.

Prerequisite

CMPS 2200 or equivalent and CMPS 2170 or equivalent, or permission of instructor.

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