MS in Computer Science: Artificial Intelligence Track | Computer Science

Students complete artificial intelligence track by substituting four of the MSCS electives with four courses designated for this track — two are required and two are electives.

1. Complete the two required courses

CS 536 - Intro to Machine Learning

This course provides a broad introduction to machine learning and its applications. Major topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); computational learning theory (bias/variance tradeoffs, VC theory, large margins); unsupervised learning; semi-supervised learning; reinforcement learning. The course will give students the basic ideas and intuition behind different techniques as well as a more formal understanding of how and why they work. The course will also discuss recent applications of machine learning, such as to data mining, bioinformatics, and information retrieval. Prerequisites: Undergraduate Algorithms, Probability with Statistical Methods. Term offered varies. 3 credits.. Levels: Graduate, Undergraduate
CS 565 - Intro to Artificial Intelligen

This course will cover the basic ideas and techniques underlying the design of artificial intelligence (AI) agents. Topics include search, knowledge representation (and reasoning), planning, reasoning under uncertainty, machine learning (including reinforcement learning), and applications (natural language processing, vision, robotics, etc). Prerequisite: Undergraduate Algorithims. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

2. Complete two of the following electives

CS 515 - Social Media Data Sci Pipeline

The focus of this course is on applying data science techniques to large-scale social media. The topics covered include large-scale data collection, cleaning, and management, exploratory analysis and measurement techniques, hypothesis testing and statistical modeling, and predictive, real time analytics. Students will build an end-to-end analysis pipeline and use it to answer questions about online events as they occur. The goal of the class is to provide students with a methodological toolbox, the technical skills to make use of these tools, and the experience of using them on real world data. Prerequisite: Undergraduate Operating Systems, Undergraduate Algorithms, Probability with Stat Methods or equivalent. Term offered varies. 3 credits Levels: Graduate
CS 517X - Intr to Human Comp Interaction

This course provides an overview of Human-Computer Interaction (HCI) and its various applications. It covers a range of important topics, including the fundamentals of HCI, basic techniques of data analysis, Mobile and Wearable Computing, Ubiquitous Computing (Internet of Things), VR/AR, Brain-Computer Interaction (BCI), Accessibility, and Smart Health. Throughout the course, students will gain a solid understanding of HCI principles, along with practical insights into recent advancements and applications. Prerequisites: CS 375 Design & Analysis of Algorithms, MATH 327 Probability & Statistics. Expected to be offered at least once a year 3 credits. Levels: Graduate
CS 524 - Intelligent Mobile Robotics

The focus of this course is on intelligent mobile robots that can autonomously operate in indoor environments with limited human guidance. The topics covered in this course include mapping, localization, navigation, planning, reasoning, and human-robot interaction (language-based and vision-based). The students will learn to develop software in Robot Operating System (ROS) on real mobile robots. The goal of this course is to help students learn entry-level algorithms and programming skills that are required to conduct research in the area of intelligent mobile robotics. Prerequisite: Undergraduate Operating Systems and Algorithms. Term offered varies. 3 credits. Levels: Graduate, Undergraduate
CS 535 - Introduction To Data Mining

Basic topics of data mining, including data preprocessing, mining association rules, classification rules, clustering rules, post processing, and mining in unstructured data. Prerequisite: Undergraduate Algorithms and Probability & Statistics or equivalent. Term offered varies. 3 credits Levels: Graduate, Undergraduate
CS 555 - Intro to Visual Info Processin

The course focuses on fundamental topics, including visual information acquisition, representation, description, enhancement, restoration, transformations and compressions, and reconstruction from projections. The second focus is on Computer Science applications, including algorithms developed in applications such as statistical and syntactic pattern recognition, robotic vision, multimedia indexing, visual data mining, and bio-informatics. Prerequisite: Undergraduate Algorithms. Term offered varies. 3 credits Levels: Graduate, Undergraduate
CS 556 - Intro to Computer Vision

Course has two parts. Part one focuses on an introduction to the fundamental topics of computer vision, including low-level vision, intermediate-level vision, high-level vision, vision systems, visual knowledge representation, motion analysis, shape from shading and 3D reconstruction, as well as image retrieval. Part two introduces the applications of the fundamental computer vision techniques. Examples include robotic vision, pattern recognition and medical imaging. Prerequisite: Undergraduate Algorithims. Term offered varies. 3 credits. Levels: Graduate, Undergraduate
CS 580E - Special Topics
CS 580P - Special Topics