BCSC: Artificial Intelligence

• 29 Mar – 31 May 2026
• Webpage

Introduction

1. 뇌의 지능을 컴퓨터에 구현하기 위한 노력을 탐구하고
2. 뇌를 모방한 신경망을 컴퓨터서 학습하기 위한 공학적 요소를 탐구합니다.
3. 최종적으로 (1)과 (2)의 bridge가 있는지 탐구합니다.

스터디는 대개, Hebb rule이 기억이 만듬을 보이고 현대 LLM 또한 같은 원리임을 보이는 방향으로 진행되었습니다.

Results

• Card News

Materials

Main Materials

• Week 1–3: Introduction to the Theory of Neural Computation — Hertz, Krogh, and Palmer
• Week 4: A Fast Learning Algorithm for Deep Belief Nets — Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh
• Week 5: Attention is All You Need — Ashish Vaswani et al.
• Week 6: Hopfield Network is All You Need — Hubert Ramsauer et al.

Additional Materials

• Week 2: https://www.youtube.com/watch?v=ozyNz5-2Ek0
• Week 3: https://www.youtube.com/watch?v=tk9FTdKOL5Q
• Week 4: Deep Learning — Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
• Week 4: https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
• Week 5: https://jalammar.github.io/illustrated-transformer/
• Week 6: Dense Associative Memory for Pattern Recognition — Dmitry Krotov and John J. Hopfield
• Week 6: https://ml-jku.github.io/hopfield-layers/

Schedule

Week	Contents (Slides)	Presenter
1	Overview & McCulloch-Pitts Neuron	윤시원
2-1	Hopfield Model I	황동욱
2-2	Hopfield Model II	정서윤
3	Boltzmann Machines	전수빈
4	Deep Learning (RBM, DBN)	정서윤
5	Attention is All You Need & Memory	황동욱
6	Hopfield Network is All You Need	김은민