How AI and the brain work (2024 spring)
[Important notice] This course is the first part of the Brain x AI lecture series:
How AI and the brain work (undergraduate course BCS441; previously BiS 429):
focuses on inverse problems. It covers linear methods, deep learning, and neuroscience of deep learning.Brain-inspired artificial intelligence (graduate course BCS541):
focuses on control problems. It covers temporal credit assignment problems, error backpropagation through time, reinforcement learning theory, algorithms, and neuroscience.I will no longer teach BiS 429 and BCE 772.
Summary. A blend of machine learning with neural science
Questions. This course focuses on the six paradoxical questions in AI and neuroscience: How does the machine/brain
translate an infinite amount of experience into a finite set of representations? (Forward-backward computation)
predict the future from the current events? (Structural-functional complexity)
implement sensitive and invariant perception? (Specificity-invariance dilemma)
learn from subjective experience objectively? (Encoding-decoding problem)
encode temporal information in spatial networks? (Episodic memory problem)
backpropagate information through time? (Error backpropagation through time)
Goal. The course consists of four modules: linear models, shallow networks, key elements of deep learning, and neuroscience of deep learning. Students in biology or brain science aim to develop the ability to explore big questions in science and relate them to the context of machine learning. Students interested in AI and computational neuroscience aim to gain biological insight into various engineering problems.
Expectation. Students are expected to understand commonalities and differences between artificial and biological neural networks (how they work). In the long run, students would gain a better insight into the theoretical issues in AI (why they work).
Instructor: Sang Wan Lee (sangwan@kaist.ac.kr)
Web page: https://aibrain.kaist.ac.kr/class-aibrain
Credit: 3 units (3:0:0)
Lecture Room: E11, #210
Time: Monday and Wednesday 10:30-12:00
Prerequisite: Linear algebra and probability (or equivalent)
Assessment: Attendance(30%), Mid-term exam(30%), Coding session(10%), Final exam(30%)
Textbook: Lecture materials (70%) + a few chapters of the following book (30%):
I. Goodfellow, et al. Deep learning, MIT press. (chapter 1-10)
