Advanced Intelligence (2019 Spring BiS400C)
[Important notice] For those who previously attended my other graduate courses, it is not recommended to sign up for this one. The previous course, Frontiers in brain-inspired AI (BiS800), is now split into the two courses :
Advanced Intelligence (undergraduate course) covers the first half, and
Brain-inspired AI (graduate course) focuses on the rest: reinforcement learning theory, algorithms, and neuroscience.
Summary: A blend of machine learning with neuroscience
The aim of this course is to understand how machine learning algorithms and brains learn to make sense of the world, which is formulated as the inverse problem:
The course provides a general introduction to linear models, neural networks, and deep learning, neuroscience of deep learning, and basics of reinforcement learning. Students are expected not only to understand how algorithms and brains work, but, more importantly, why they work.
Lecture Room: #207 (E16 ChungMoonSul Bldg.)
Time: Monday and Wednesday 13:00-14:30
Instructor: Sang Wan Lee (firstname.lastname@example.org, #516 E16-1)
Office Hours: Tuesday and Thursday 11:00 – 11:55
Credit: 3 units (3:0:0).
Prerequisite: Linear algebra and probability (or equivalent)
Assessment: Attendance (40%), mid-term exam (40%), term projects (20%)
Textbook: Lecture materials (70%) + a few chapters of the followings (30%):
[T1] John Shawe-Taylor, Nello Cristianini, Kernel Methods for Pattern Analysis.
[T2] I. Goodfellow, et al. Deep learning, MIT press.