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 (E16-1 YBS 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.
Lecture slides, announcements, and etc.
Matrix algebra essentials : Ax=b
Singular value decomposition
Inequalities and maximization lemma
Least squares estimators
Neural networks (I) : basics
Feedforward neural networks
Statistical learning theory
Optimal neural networks (support vector machines)
Neural networks (II) : kernel
Optimal neural networks (kernel support vector machines)
Neural networks (III) : hands-on session
— Midterm exam —
Deep learning (I) : algorithms
Convolutional neural networks
Training, sparsity, information bottleneck theory
Deep learning (II) : advanced topics
Generative adversarial networks
Recurrent neural networks
Deep learning (III) : hands-on session
Neuroscience of deep learning (I) : basics
Bottom-up cortical information processing
Top-down cortical information processing
Neuroscience of deep learning (II) : advanced topics
Selectivity and dimensionality of cortical activity
Optimization in cortical networks
Reinforcement learning : basics*
Markov decision process
Model-free prediction and control
(*This is a prerequisite for the graduate course, Brain-inspired AI (BCE772).)
Reinforcement learning : hands-on session
— Final exam week (Term project report due) —