Computer Science

This course examines the use of data science tools to summarize, visualize, and analyze data. Sensible workflows and clear interpretations are emphasized.

This course examines concepts and principles of database management systems. Topics covered include basic concepts, system structures, data models, database languages (SQL), relational database normalization, file systems, indexing, query processing, concurrency control, and recovery schemes.

This course provides an overview of machine learning, a core technology of artificial intelligence. It begins with fundamental mathematical concepts related to linear algebra and probability theory, and then introduces key machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning.  

The course covers fundamental concepts and principles of machine learning algorithms, analysis of real-world data using machine learning techniques and programming tools, and development of machine learning solutions for real-world problems in various domains. 

Topics include Parametric Density Estimation, Linear Regression, Classification and Logistic Regression, Generative Learning Algorithm, Deep Learning and Neural Networks, Generalization and Regularization, Clustering and K-means Algorithm, Dimensionality Reduction, Generative Models, and Markov Decision Process and Reinforcement Learning. 

Prerequisites: Probability, Linear Algebra, Python 

The course is a continuation of CS 141A MACHINE LEARNING A course and provides deeper theoretical foundations of machine learning and a number of advanced theoretically grounded learning techniques. A tentative list of topics includes: basics in optimization theory, basics of information theory, advanced techniques for analyzing generalization power of learning algorithms, Kernel methods, ensemble classifiers and weighted majority vote, and Bayesian inference. Prerequisite: CS 141 MACHINE LEARNING A. 

This course introduces computer programming in C++. The course covers the functional elements of a computer system, object-oriented programming concepts, problem solving and creation of computer applications. Students apply these computing skills in various disciplines. This course provides a foundation to further study in advanced computing topics.

This course offers an advanced introduction into web recommender systems. The goal is to understand and model Web Information and to design and evaluate some of the major technologies operating in the area of web recommender systems through applied projects. Topics include basics of recommender systems (collaborative filtering and content based); evaluation of recommender systems; advanced recommender systems (knowledge-based, ensembled based, hybrid); exploiting additional sources of information for recommendation, e.g., context, location and time.

This course covers the basics of character animation, keyframe animation, motion capture, inverse kinematics, and physically based character animation. Also the basics of physically-based animation, rigid body dynamics, point-based dynamics, hair animation, cloth simulation, facial animation, crowd simulation, mesh-shape editing, performance capture, skinning, data-driven character control, data-driven cloth animation, data-driven facial animation, and data-driven skinning. Prerequisite: COMP2119.

This course provides an overview of computer systems security. Students build a comprehensive understanding of central problems in software and hardware security and solutions to them. 

Topics include the basic concepts of threats and security, access control, authentication, cryptography, and low-level software and hardware security. In addition to the principles and theories presented in the class, students are able to develop practical skills by completing six programming assignments along the way. Students perform threat modeling and security analysis of existing computer systems, write attack payloads that compromise insecure programs, and develop defensive programming skills and be able to use existing defensive mechanisms.

Prerequisite: Operating Systems; Computer Architecture

This course introduces the foundations of reasoning under uncertainty, its characteristics and the effect of these on inference processes. Topics include modeling, using and designing fuzzy expert systems, Bayesian networks, stochastic knowledge, decision theory, Markov decision processes, and Kalman filtering.

This course examines heuristic search, games, and algorithms. Topics include the importance of heuristics in search problems, search algorithms, evolutionary programming, evaluating heuristics, and game problems. Students examine NP-completeness theory, classifying problems, graph matching, and information theory.