Sub TA:
YeonJi Song, YunHyeok Kwak, YouWon Jang, JunSeok Park, JungHyun Kim
Objectives:
This course will introduce basic ideas and techniques underlying the design of intelligence computer systems. A specific emphasis will be on the basics of statistical and decision-theoretic modelling paradigm in the field of artificial intelligence.
Upon successful completion of this course, students will be able to:
Study a wide range of artificial intelligence theory, techniques and systems about machines that behave and think like people.
Understand concepts, models, and algorithms to develop intelligent agents such as navigation, reasoning, planning, knowledge representation, decision making, learning, visual and language.
Learn and practice key technologies of artificial intelligence such as empirical search, Bayesian network, Hidden Markov network, and reinforcement learning.