| CS229 2019 and Berkeley CS182 | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Order | Theme | Date | Presenter | Lecture | Practice | T.A. | Reading Materials | Slides | Video |
| 1 | Linear Algebra, Probabilities, ML | 2022.07.02 | 신재요 | 주재걸 교수님 - Four Views of Matrix Multiplication, Linear Independence, Linear Transformation | Practice 1,2 | 이병근 | drive link | drive link | drive link |
| 2 | Linear Algebra, Probabilities, ML | 2022.07.06 | 임상범 | 주재걸 교수님 - Least Squares | Practice 3,4 | 이병근 | drive link | drive link | |
| 3 | Linear Algebra, Probabilities, ML | 2022.07.09 | 최새미 | 주재걸 교수님 - Eigendecomposition, SVD | Practice 5,6 | 이병근 | drive link | drive link | drive link |
| 4 | Linear Algebra, Probabilities, ML | 2022.07.13 | 이용권 | CS 229 lec 2 - Review of Matrix Calculus, Review of Probability | Practice 7 | 이병근 | drive link1 drive link2 |
drive link | drive link |
| 5 | Linear Algebra, Probabilities, ML | 2022.07.16 | 임상범 | CS 229 lec 3 - Review of Probability and Statistics, Setting of Supervised Learning | X | 이병근 | drive link | drive link | drive link |
| 6 | Linear Algebra, Probabilities, ML | 2022.07.20 | 최설아 | CS 229 lec 4 - Linear Regression (Normal Equations, probabilistic interpretation), MLE | X | 이관호 | drive link | drive link | drive link |
| 7 | Linear Algebra, Probabilities, ML | 2022.07.23 | 김원균 | CS 229 lec 5 - Logistic Regression, Newton's Method, CS 229 lec 21 - Evaluation Metrics (F1, ROC, etc..) | X | 박민호 | drive link | drive link | drive link |
| 8 | Deep Learning | 2022.07.27 | 윤정인 | lec 1-2 (Introduction, ML basics 1) Discussion 1 | X | 이관호 | X | drive link | drive link |
| 9 | Deep Learning | 2022.07.30 | 최설아 | lec 3-4 (ML basics 2, optimization) Discussion 2 | hw1 | 이관호 | Distill: momentum OpenAI: Deep double descent Mathematics for Machine Learning (p.291-p.303) |
CS182 slides | drive link |
| 10 | Deep Learning | 2022.08.03 | 신재요 | lec 5-6 (Backpropogation, CNN) Discussion 3 | hw1 | 이관호 | drive link | ||
| 11 | Deep Learning | 2022.08.06 | 이재성 | lec 7-8(Getting neural nets to train, Computer Vision) Discussion 4 | X | 박민호 | Weng's Blog: Overfitting in deep neural network | drive link | drive link |
| 12 | Deep Learning | 2022.08.10 | 김현탁 | lec 9 Generating images from CNN, lec 10 RNN Discussion 5 | X | 박민호 | Baek's medium: RNN and Regularization (Dropout) Dive into Deep Learning: Bidirenctional RNN AI 꿈나무's Blog: Seq to Seq Machine Translation LittleFox's Blog: Beam Search |
CS182 slides | drive link |
| 13 | Deep Learning | 2022.08.13 | 윤정인 | lec 11 Seq2Seq Discussion 6 | X | 이관호 | drive link | ||
| 14 | Deep Learning | 2022.08.17 | 김동후 | lec 12 Transformers Discussion 7 | hw3 | 이관호 | HarvardNLP's Blog: Transformer | drive link | |
| 15 | Deep Learning | 2022.08.20 | 정하원 | lec 13 NLP applications Discussion 8 (pretraining) | hw3 | 이관호 | drive link | ||
| 16 | Deep Learning | 2022.08.24 | 김상우 | Information Theory 1~3 (Entropy, Cross-Entropy, KL Divergence) Youtube | hw2 | 박민호 | drive link | drive link | |
| 17 | Deep Learning | 2022.08.27 | 박민호 | lec 17 (Autoencoder & Latent variable model), lec 18 VAE part2 Discussion 10 | hw2 | 박민호 | CS182 slides | drive link | |
| 18 | Deep Learning | 2022.08.31 | 허경훈 | lec 18 (VAE), lec 19 (GAN) Discussion 11 | hw2 | 박민호 | CS182 slides | drive link | |
| 19 | Deep Learning | EXAM |
Information Theory 1~3 (Entropy, Cross-Entropy, KL Divergence)
by Sangwoo Kim (김상우)
