Graduate Computer Science Courses

COSC 5086. Advanced Special Problems in Computer Science. 1-6 Credit Hours (Lecture: 1-6 Hours, Lab: 0 Hours).

Advanced special problems in computer science. Work may be either theory or laboratory. May be repeated with approval of the department head for additional credit.

COSC 5088. Thesis Research. 1-6 Credit Hours (Lecture: 1-6 Hours, Lab: 0 Hours).

Research for Master’s thesis in AI and Machine Learning (AIML-MS).

COSC 5330. Simulation. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

Introduction to simulation with emphasis on simulation methodology, random number generation, time flow mechanisms, sampling techniques, and validation and analysis of simulation models and results. Simulation languages and their applications will be investigated.

COSC 5345. Reinforcement Learning. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

This course will provide an introduction to, and comprehensive overview of, reinforcement learning (RL). Topics include Markov decision process and dynamic programming, Monte-Carlo methods, temporal difference learning, integration of planning and learning, policy gradient and actor-critic methods, deep learning and deep RL algorithms. Students will engage in exercises and projects that involve coding in simulated RL environments. Credit will not be awarded for both COSC 4345 and 5345. Graduate students will have to complete additional assignments. Prerequisite: Advanced background in statistics and artificial intelligence.

COSC 5346. Robotics and Autonomous Systems. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

Overview of the major areas of robotics and autonomous systems. AI, machine learning and optimization algorithms that enable autonomous agents to operate in unstructured, dynamic environments, including localization and mapping, sensor fusion, computer vision, path planning, communication, and obstacle avoidance. Students will engage in exercises and projects that involve developing robotics systems with autonomous actions, and evaluating their performance using computer simulations and physical robotic systems. Credit will not be awarded for both COSC 4346 and 5346. Graduate students will have to complete additional assignments. Prerequisite: Advanced background in statistics, linear algebra and artificial intelligence.

COSC 5347. High Performance Computing. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

This course provides an introduction to programming massively parallel processors and the architectures therein. It covers methods to harness the potential of Graphical Processing Units (GPUs) and parallel algorithms using the CUDA parallel computing platform. Algorithms from the fields of Scientific Computing, Machine Learning, and Computer Vision are introduced and explored.

COSC 5352. Optimization for Machine Learning. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

This course will explore the theory and algorithms that arise in machine learning and modern data analysis. The topics will be tailored with a particular focus on complexity, implementation, robustness, and scalability of algorithms to large datasets. Students will engage in exercises and projects that involve programming optimizations algorithms, and evaluating their performance.

COSC 5360. Artificial Intelligence. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

Introduces representations, algorithms and architectures used to build intelligent systems. Predicate calculus, state-space representation and search, heuristic search, knowledge-based problem-solving, symbol-based and connectionist machine learning, intelligent agents, robotics.

COSC 5361. Deep Neural Networks. 3 Credit Hours (Lecture: 3 Hours, Lab: 0 Hours).

Introduction to the principles and theory of neural networks, with emphasis on deep neural networks. Topics include convolutional networks, recurrent and LSTM networks, reinforcement learning, preprocessing, regularization, tuning and optimization, as well as mathematical and programming tools. Applications to classification, image recognition, autonomous vehicles. Credit will not be awarded for both COSC 4361 and 5361. Graduate students will have to complete additional assignments. Prerequisite: Advanced background in statistics, linear algebra and artificial intelligence.