Laboratory for Brain and Machine Intelligence

Laboratory for Brain and Machine Intelligence @ KAIST

Laboratory for brain and machine intelligence, KAIST

Frontiers in brain-inspired AI (2018 Spring Bis800)


Course Summary

How do machines and brains solve optimal control problems? This course will explore frontiers in modern brain-inspired artificial intelligence. The first part of our course discusses how to solve the inverse problem:

Ax=y.

Topics include neural networks, deep learning, and cortical information processing. The second part discusses how to solve the optimal control problem:

f(y(t), x(t))=y(t+1).

Students are expected to learn about basic theory, algorithms, and neuroscience of reinforcement learning (RL). We will also have in-depth discussions of recent advances in neuroscience of RL and deep RL.

Lecture Room

#215 (E16 ChungMoonSul Bldg.)

Time

Monday and Wednesday 10:30-12:00

Instructor

Sang Wan Lee (sangwan@kaist.ac.kr, Rm.#516 E16-1)

Office Hours

Tuesday and Thursday 11:00 – 11:55

Teaching Assistant

TBA

Credit

3 units (3:0:0).

Prerequisite

Linear algebra and probability (or equivalent)

Assessment

Attendance (30%), mid-term exam (20%), presentation (30%), final term projects (20%)

Textbook

Lecture materials (70%) + a few chapters of the followings (30%):
- S. Haykin, Neural Networks and Learning Machines, Prentice Hall, 2009.
- R. S. Sutton and A. G. Barto, An Introduction to Reinforcement Learning, MIT Press, 1998.
- D. Bertsekas, Dynamic Programming and Optimal Control: Approximate dynamic programming (Volume 2), Athena Scientific, 2012.


Lecture slides, announcements, and etc.

KAIST KLMS


Lecture schedule

Bis800_schedule.PNG