Computer Science

In this course, students learn how images are formed, how they are represented on computers, and how they can be processed by computers to extract semantic information. Students develop algorithms for detecting interesting features in images, design neural networks to perform natural image classification, and explore algorithms for solving real-world problems such as hand-written digit recognition and object detection.  

This course examines various aspects of data processing including database management, representation and analysis of data, information retrieval, visualization and reporting, and cloud computing. 

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.