100 Levels

AI Basics

Some essential basics for the research in artificial intelligence and data sciences: Topics include Linear algebra, symbol processing, optimization algorithms, reasoning, and imperative programming

PYTHON I

It covers details of how to start and stop the interpreter and write programs: Topics include datatypes, files, functions, error handling, tuples, lists, dictionaries, and sets. Students will also learn how to effectively use Python’s very powerful list processing primitives such as list comprehensions.

PYTHON II

It promotes more advanced programming skills for Artificial Intelligence and data sciences: Topics include how to design functions that are reliable and can be easily reused across files, and some useful standard library modules for AI.

Cognitive Science and AI

This course is an introduction to computational theories of human cognition. Topics include concept learning, reasoning, the structure and formation of natural language (syntax and semantics), which are closely connected to learning Bayesian Inference, first-order logic, and graphical models.

Reasoning

This course is more dedicated to learn the beauty of reasoning on the knowledge representation. In particular, propositional logics, first-order logics and probabilistic inferences are widely studied. A logic programming is essentially carried out to develop an intelligent reasoning agent.

AI Application Design

This course is only designed for the first-year student, to apply all their knowledge and skills to make a first AI product or concept. A self-managing project is the key course objective, and the professors will couch the student to end up to the final demonstration which will be publicly opened to others.

AI Project Capstone #1-1 (The Spring semester at Year 1)

This final semester research project is to complete a particular industrial problem given to the students. All data science and AI methods should be applied to address this problem. The final demonstration will be pitched before the industry people. A prospective scholarship from the industry may be offered, in this case, the student carries on the industry-focussed problem throughout their undergraduate studies

AI Project Year #1 (The Fall Semester at Year 1)

The first-year students only are taking this course to present their skills and expertise to the industry partners. The industry partners will provide comments and insights of how to develop them, and if preferred, the AI project would be selected for the future funds by the industry partners.

Lab Internship I

School of Intelligence offers summer or winter internships to first-year students. This is open only to first-year students, and, PYTHON I/II are essential requirement. These are programming internships, and the tasks will be adjusted to fit the research project.

200 Levels

Computational Algorithms

The use of randomness in algorithm design is an extremely powerful paradigm. Often, it makes algorithm design (and analysis) easier. This course introduce the basic techniques of designing randomized algorithms although at times we will dive into state-of-the-art topics, including computational methods of optimization, dynamic programming, and NP-hard problems.

Pre-requisites: At least B at AI Basics, Python I & II

Bayesian Theory

This course discusses the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. Topics includes Bayesian statistics, Bayesian inferences, decision-making and Bayesian regressions.

Probability (Graph) Theory

Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. This course covers Bayesian networks, undirected graphical models and their temporal extensions; exact and approximate inference methods; estimation of the parameters and the structure of graphical models.

Pre-requisites: At least B at Bayesian Theory

AI Project Capstone #2-1/#2-2/#2-3

These research projects are to address industrial problems given to the students. All data science and AI methods should be applied to find a better and reliable solution. The demonstration of their research outcome will be pitched before the industry people. A prospective scholarship from the industry may be offered

Pre-requisites: At least B at AI Project Capstone #1-1, and at least B at the other previous #2-x

Lab Internship II

School of Intelligence offers summer or winter internships to second-year students. This is open only to second-year students, and, Computational algorithms and Bayesian Theory are essential requirements. These are programming internships, and the tasks will be adjusted to fit the research project.

300 Levels

Problem-solving AI

This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition. Topics include: (i) Supervised learning (ii) Unsupervised learning (iii) Best practices in machine learning. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply machine learning algorithms to build practical solutions.

Pre-requisites: At least B at Computational Algorithms, Bayesian Theory

Knowledge-based AI

This course covers how to infer new knowledge based on previous information, and topics include the basics of state space search problems (Hill Climbing, Heuristics search etc.), knowledge representation technique, monotonic & non-monotonic reasoning.

Pre-requisites: At least B at Computational Algorithms, Reasoning

Deep Feedforward Learning

This course covers modern deep-learning based generative models with an emphasis on breadth. It will present four categories of deep generative models: autoregressive models, normalizing flows, variational auto-encoders, and adversarial generative models.

Pre-requisites: At least B at Computational Algorithms, Bayesian Theory

Vision and Object Recognition

This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis.

Pre-requisites: At least B at Computational Algorithms, Bayesian Theory, Probability (Graph) Theory

AI Project Year #3 (The Fall Semester at Year 1)

The third-year students only are taking this course to present their skills and expertise to the industry partners. The industry partners may buy out your research project, and can connect to the industry internship course at Year 4.

Lab Internship III

School of Intelligence offers summer or winter internships to third-year students. This is open only to third-year students, and, Deep Feedforward Learning is the essential requirement. These are programming internships, and the tasks will be adjusted to fit the research project.

400 Levels

Action Learning

Students will learn how to create digital product experiences powered by AI and big data that deliver high levels of cognitive experiences to users. Students will form teams of five to build a concept for an AI product for the duration of the course, preparing for two final presentations.

Pre-requisites: At least B above at all 300 level courses

Advanced Tech in AI

The course goes in depth on selected topics and methods within artificial intelligence (AI), machine learning (ML) and their applications. Examples include computational intelligence algorithms in search, optimization and classification, which to a large extent consist of bio-inspired mechanisms. Examples of relevant applications include robotics, music, health and medicine.

Pre-requisites: At least B above at all 300 level courses

Interdisciplinary AI Research

Different subfields of AI (such as vision, learning, reasoning, and planning) are often studied in isolation, both in individual courses and in the research literature. In practice, developing integrated AI systems remains an open challenge for both research and industry. Interdisciplinary project-driven courses can fill this gap in AI education, providing challenging problems that require the integration of multiple AI methods.

Pre-requisites: At least B above at all 300 level courses

Co-op work experience

Industry AI Research (single or two semesters)

Several (Korea and International) industry partners will be invited to recruit the students based on their expertise, skill set, and enthusiasm. During one or two semesters, the students selected from the industry partners work at the places where the real AI problems take place. This covers 15 course credit per semester, and all the assessment will be made by the industry partners. This normally carries out in the final year of the undergraduate study.