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Students complete this track by substituting four of the BSCS electives by four courses designated for this track — two are required and two are electives. 

1. Complete the two required courses

• CS 436 - 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: CS 375 and MATH 327 or MATH 448 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 465 - 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: CS 375 (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

2. Complete two of the following electives

• CS 415 - 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 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: CS 350 Operating Systems, CS 375 Design & Analysis Algorithm, MATH 327 Probability with Stat Methods or equivalent. (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 417X - 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 4 credits. Levels: Undergraduate

• CS 424 - 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: CS 350, CS 375. (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate Restrictions: Must be enrolled in one of the following Majors:            Computer Science

• CS 435 - 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. Prerequisites: CS 375, MATH 304 and MATH 327 or MATH 448 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 455 - 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: CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 456 - 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. Pre req CS 375 (All prerequisites must have a grade of C- or better). 4 credits Levels: Undergraduate

Also available as 4-credit special topics courses: CS 480D (Computational Social Science), CS 480P (Introduction to Natural Language Processing) and CS 480E (Introduction to Deep Learning)