Discipline ID
bf91b86a-62db-4996-b583-29c1ffe6e71e
COURSE DETAIL
BIOINFORMATICS
Country
Hong Kong
Host Institution
University of Hong Kong
Program(s)
University of Hong Kong
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
153
UCEAP Course Suffix
UCEAP Official Title
BIOINFORMATICS
UCEAP Transcript Title
BIOINFORMATICS
UCEAP Quarter Units
5.00
UCEAP Semester Units
3.30
Course Description
This course provides a grounding in basic bioinformatics algorithms, tools, and databases. The course also provides hands-on bioinformatics analysis experience and the ability to conduct independent bioinformatics analyses. Topics: algorithms, especially those for sequence alignment and assembly, which comprise the foundation of the rapid development of bioinformatics and DNA sequencing; leading bioinformatics tools for comparing and analyzing genomes starting from raw sequencing data; the functions and organization of a few essential bioinformatics databases and how they support various types of bioinformatics analysis.
Language(s) of Instruction
English
Host Institution Course Number
COMP3353
Host Institution Course Title
BIOINFORMATICS
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science
Course Last Reviewed
COURSE DETAIL
NATURAL COMPUTING
Country
United Kingdom - Scotland
Host Institution
University of Edinburgh
Program(s)
University of Edinburgh
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
120
UCEAP Course Suffix
UCEAP Official Title
NATURAL COMPUTING
UCEAP Transcript Title
NATURAL COMPUTING
UCEAP Quarter Units
4.00
UCEAP Semester Units
2.70
Course Description
This course teaches about bio-inspired algorithms for optimization and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimization, ant colony optimization as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search large parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward, or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.
Language(s) of Instruction
English
Host Institution Course Number
INFR11161
Host Institution Course Title
NATURAL COMPUTING
Host Institution Course Details
Host Institution Campus
University of Edinburgh
Host Institution Faculty
Host Institution Degree
Host Institution Department
School of Informatics
Course Last Reviewed
2022-2023
COURSE DETAIL
ALGORITHM ANALYSIS
Country
Korea, South
Host Institution
Yonsei University
Program(s)
Yonsei University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
110
UCEAP Course Suffix
UCEAP Official Title
ALGORITHM ANALYSIS
UCEAP Transcript Title
ALGORITHM ANALYSIS
UCEAP Quarter Units
4.50
UCEAP Semester Units
3.00
Course Description
An algorithm is a well-defined mechanical procedure that solves a computational problem (Cormen et al., 1990). This course introduces students to elementary algorithm design techniques including divide and conquer, dynamic programming, greedy algorithms, and network flow, together with the mathematical proof techniques used to establish the correctness of algorithms. The course also covers NP-completeness. Textbook: J. Kleinberg and E. Tardos, ALGORITHM DESIGN. Prerequisites: Data Structure, DISCRETE MATHEMATICS
Language(s) of Instruction
English
Host Institution Course Number
CSI3108
Host Institution Course Title
ALGORITHM ANALYSIS
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science
Course Last Reviewed
2024-2025
COURSE DETAIL
PRINCIPLES AND PRACTICES OF SOFTWARE DEVELOPMENT
Country
Korea, South
Host Institution
Seoul National University
Program(s)
Seoul National University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
123
UCEAP Course Suffix
UCEAP Official Title
PRINCIPLES AND PRACTICES OF SOFTWARE DEVELOPMENT
UCEAP Transcript Title
SOFTWARE DEVELOPMNT
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
This course teaches the principles of software development for medium to large software design and implementation. Students apply these principles to software systems in practice by working on group projects. Through this experience, students learn how to build correct and high-performance software.
Language(s) of Instruction
English
Host Institution Course Number
M1522.002400
Host Institution Course Title
PRINCIPLES AND PRACTICES OF SOFTWARE DEVELOPMENT
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science and Engineering
Course Last Reviewed
2021-2022
COURSE DETAIL
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Country
Korea, South
Host Institution
Yonsei University
Program(s)
Yonsei University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
121
UCEAP Course Suffix
UCEAP Official Title
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
UCEAP Transcript Title
ARTIFICIAL INTEL
UCEAP Quarter Units
4.50
UCEAP Semester Units
3.00
Course Description
Computational (data-driven) AI is one of the fastest-growing and most exciting fields lately, and machine learning represents its genuine bleeding edge. In this course, you'll develop a clear understanding of the motivation for machine learning, and design intelligent systems that learn from data. Emphasis is placed on the analysis of these models, on methods of training them and on their application to engineering problems in prediction, regression, and classification. Prerequisites: Calculus, Linear Algebra, Probability Theory and Random Variables, Matlab/Python/C/C++ (either one) Programming skill.
Language(s) of Instruction
English
Host Institution Course Number
EEE3314
Host Institution Course Title
INTRODUCTION ARTIFICIAL INTELLIGENCE
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Electrical and Electronics Engineering
Course Last Reviewed
COURSE DETAIL
DEEP NEURAL NETWORKS
Country
Germany
Host Institution
Technical University Berlin
Program(s)
Technical University Berlin
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
125
UCEAP Course Suffix
UCEAP Official Title
DEEP NEURAL NETWORKS
UCEAP Transcript Title
DEEP NEURAL NETWORK
UCEAP Quarter Units
3.00
UCEAP Semester Units
2.00
Course Description
This course examines deep neural networks (DNN) and covers the following topics: DNN basics, model complexity and model selection, optimization of DNNs, DNNs for images, DNNs for time series, explaining DNN decisions, DNNs beyond classification.
Language(s) of Instruction
English
Host Institution Course Number
Host Institution Course Title
DEEP NEURAL NETWORKS
Host Institution Course Details
Host Institution Campus
FAKULTÄT IV ELEKTROTECHNIK UND INFORMATIK
Host Institution Faculty
Host Institution Degree
Host Institution Department
Informatik
Course Last Reviewed
COURSE DETAIL
HEALTH DATA SCIENCE AND DATA ANALYTICS IN HEALTHCARE (LEVEL 2)
Country
United Kingdom - England
Host Institution
University College London
Program(s)
Summer at University College London
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Health Sciences
Computer Science
UCEAP Course Number
114
UCEAP Course Suffix
S
UCEAP Official Title
HEALTH DATA SCIENCE AND DATA ANALYTICS IN HEALTHCARE (LEVEL 2)
UCEAP Transcript Title
HEALTH DATA SCIENCE
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
Health Data Science is an area that combines scientific inquiry, statistical knowledge, substantive expertise, and computer programming in the area of healthcare and biomedicine. Students are introduced to fundamental data analytic tools and techniques, and learn how to use specialized software to analyze real-world health data.
Language(s) of Instruction
English
Host Institution Course Number
ISSU0091
Host Institution Course Title
HEALTH DATA SCIENCE AND DATA ANALYTICS IN HEALTHCARE (LEVEL 2)
Host Institution Course Details
Host Institution Campus
Bloomsbury
Host Institution Faculty
Host Institution Degree
Bachelors
Host Institution Department
Institute of Health Informatic
Course Last Reviewed
2022-2023
COURSE DETAIL
ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE
Country
Singapore
Host Institution
National University of Singapore
Program(s)
National University of Singapore
UCEAP Course Level
Graduate
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
208
UCEAP Course Suffix
UCEAP Official Title
ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE
UCEAP Transcript Title
ADV TOPICS IN AI
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
This course covers advanced topics in artificial intelligence that are of current research or application interests. A wide range of topics may be covered including soft computing (fuzzy logic, genetic algorithms, etc.), data mining, machine learning, image and video processing, artificial life, robotics, etc. The exact topics to be taught will depend on the lecturers teaching the module. Graduate Research Module, this semester with a focus on "Computer Vision for Self-Driving Cars". Paper presentations throughout the semester and a final capstone research project.
Language(s) of Instruction
English
Host Institution Course Number
CS6208
Host Institution Course Title
ADVANCED TOPICS IN AI
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science
Course Last Reviewed
COURSE DETAIL
BIG DATA AND ARTIFICIAL INTELLIGENCE
Country
Spain
Host Institution
Pompeu Fabra University
Program(s)
21st Century Barcelona
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
119
UCEAP Course Suffix
UCEAP Official Title
BIG DATA AND ARTIFICIAL INTELLIGENCE
UCEAP Transcript Title
BIG DATA & AI
UCEAP Quarter Units
1.50
UCEAP Semester Units
1.00
Course Description
This course offers an introduction to the concept of Big Data and how to manage large volumes of data using machine learning. It applies Big Data to various areas including education, health, finance, and social media. This course also discusses the challenges of Big Data including data bias, transparency and accountability of the algorithms, the loss of privacy and the new ethical dilemmas these algorithms generate.
Language(s) of Instruction
English
Host Institution Course Number
59029
Host Institution Course Title
BIG DATA AND ARTIFICIAL INTELLIGENCE
Host Institution Course Details
Host Institution Campus
Ciutadella Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
UPF Education Abroad Program
Course Last Reviewed
COURSE DETAIL
ARTIFICIAL INTELLIGENCE FOR GAME PROGRAMMING 2
Country
Sweden
Host Institution
Uppsala University
Program(s)
Uppsala University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
118
UCEAP Course Suffix
UCEAP Official Title
ARTIFICIAL INTELLIGENCE FOR GAME PROGRAMMING 2
UCEAP Transcript Title
AI/GAME PROGRAMMG 2
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
This course focuses on the development of software for Artificial Intelligence (AI) for computer games, primarily regarding strategic games. The course covers the development of AI for games with perfect information (e.g., chess, Othello, and AlphaGo), and games without perfect information (e.g. card and dice games), including simultaneous games and classical concepts within game theory, such as the Nash equilibrium. The programming language used is C++.
Language(s) of Instruction
English
Host Institution Course Number
5SD810
Host Institution Course Title
ARTIFICIAL INTELLIGENCE FOR GAME PROGRAMMING 2
Host Institution Course Details
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science
Course Last Reviewed
2021-2022