Week | Date | Lecturer | Contents | Materials |
---|---|---|---|---|
01 | 2022-01-09 | Yeji Park | The Notion applied to ML | Slides |
01 | 2022-01-09 | Tae-Geun Kim | Chapter 1 of LFD (Part 1) | Slides |
02 | 2022-01-16 | Sujin Lee | Chapter 2 of D2L | |
03 | 2022-01-24 | Sujin Lee | Chapter 3 of D2L | |
03 | 2022-01-24 | Tae-Geun Kim | Chapter 1 of LFD (Part 2) | Slides |
03 | 2022-01-24 | Sangho Ahn | Review transfer learning | |
04 | 2022-02-06 | |||
05 | 2022-02-14 | |||
06 | 2022-02-19 |
-
Learning From Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, AMLbook.com (2012)
-
Dive into Deep Learning, Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J., arXiv preprint arXiv:2106.11342 (2021)
-
만들면서 배우는 파이토치 딥러닝, 오가와 유타로 저, 박광수 옮김, 한빛미디어 (2021)
-
The Elements of Statistical Learning, T. Hastie, R. Tibshirani and J. Friedman, 12th edition, Springer (2017)
-
A Probabilistic Theory of Pattern Recognition, Luc Devroye, László Györfi and Gábor Lugosi, Springer (1996)
-
Machine Learning An Algorithmic Perspective, Stephen Marsland, CRC Press (2015)
-
Neural Networks and Deep Learning: A Textbook, Charu C. Aggarwal, Springer (2018)
-
Machine Learning A Bayesian and Optimization Perspective, Sergios Theodoridis, Elsevier (2020)
-
Deep Learning with PyTorch, Eli Stevens, Luca Antiga, and Thomas Viehmann, Manning Publications Co. (2020)