Computer Science

This course enables students with no technical background to have a general understanding and a taste of hands-on exploration of Artificial Intelligence (AI) and Machine Learning (ML). The course covers the basic concepts, problems, approaches and applications of AI components and systems. It provides an introduction to various topics in AI systems and technologies, e.g., an overview of AI, data representation and visualization, basics of ML, ethical and legal issues with AI, etc. It discusses the applications of engineering principles to selected AI and ML problems, including image classification, machine translation, and voice cloning. It also explores the future possibilities and challenges of AI.

This course examines a range of machine learning tools and techniques for analyzing data and automatically generating applications. The course will address tools for classification, regression, clustering and text mining, and techniques for preprocessing data and analyzing the results of machine learning tools. 

In many projects, it is important for programmers to have fine control over low-level details of program execution, and to be able to assess the cost of a design decision on likely overall program performance. This course introduces students to a system programming language that gives programmers this kind of control, explores a range of standard data structures and algorithmic techniques, and shows how to apply them to frequently encountered problems.

This course focuses on the theory of linear models. Topics include: linear regression model, general linear model, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, multiple comparisons, and an introduction to generalized linear models. This course has a prerequisite of Regression Analysis.

In this course we will examine how existing network layers are protected, how to verify the security of a protocol, and how to improve the dependability. We will specifically learn about the common vulnerabilities in the current Internet, such as botnets, viruses, denial-of-service attacks, etc., and design principles to overcome these issues in the future. We will also learn about the security benefits and challenges of network virtualization technologies, about air-gapping, as well as automated network testing methods and fuzzing.

Students work in a multidisciplinary team to develop a game or graphics system up to release quality. The course brings together practical development and theoretical analysis to ensure students know both how to make games and how to assess them.

This course examines algorithms and representational schemes used in artificial intelligence, AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks, tasks such as game playing (adversarial search), planning, and natural language processing. It discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.

This course aids in the acquisition of basic knowledge about algorithms and data structures. It discusses and instructs on evaluation methods and programming techniques for making good programs.
 

Prerequisites for regular course students are "Practice of Information Processing" and "Computer Seminar I." Taking "Fundamentals of Information Science I" is strongly recommended. Prerequisites for JYPE/DEEP/IMAC-U students are similar to the courses above.
Students should have some knowledge of computer languages, preferably C or Python.

This course focuses on prototyping an internet-based communication system. In the course, a solution is implemented that uses a modular system where the students can set their own design goals.  Assumed knowledge in programming is required.

This course starts with a historical overview of computer simulations in science and engineering and an introduction to the challenges and opportunities in connecting simulations, theory, and experiments. Students address the core concepts essential to understand and interpret computer simulations in science and engineering, including the fundamentals of statistical physics, interaction potentials, Monte Carlo simulations, equation-based simulations, and the concept of coarse-grained simulations and enhanced sampling techniques.