---
title: 2025 Fall - 機器學習
tags: Syllabus
GA: G-77TT93X4N1
---

# Syllabus -- 機器學習

## Instructor

* Instructor: 林得勝
Office: SA242 ext. 56422
Email: [e-mail]
Office Hours: Wed. 13:00-14:00
* Teaching Assistant: 張哲嘉
Email: [e-mail]
* Teaching Assistant: 戴晨洋
Email: [e-mail]
* Teaching Assistant: 洪瑋駿
Email: [e-mail]

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## Course information

> There is no required textbook for this course.

### Reference:
* Books
1. [Stanford CS229 lecture notes](https://cs229.stanford.edu/main_notes.pdf)
2. [Francis Bach - Learning Theory from First Principles](https://www.di.ens.fr/~fbach/ltfp_book.pdf)
* Journal papers
- [x] [Deep Learning: An Introduction for Applied Mathematicians](https://epubs.siam.org/doi/10.1137/18M1165748)
- [x] [On the approximation of functions by tanh neural networks](https://www.sciencedirect.com/science/article/pii/S0893608021003208)
- [x] [Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians](https://epubs.siam.org/doi/10.1137/23M1626232)
- [x] [Score-Based Generative Modeling Through Stochastic differential equations](https://arxiv.org/pdf/2011.13456)
- [x] [Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations](https://www.sciencedirect.com/science/article/pii/S0021999118307125)
- [x] [The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems](https://link.springer.com/article/10.1007/s40304-018-0127-z)
- [ ] [Neural Ordinary Differential Equations](https://proceedings.neurips.cc/paper_files/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf)
- [ ] [Diffusion maps](https://www.sciencedirect.com/science/article/pii/S1063520306000546)

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* [Grading Policy](https://hackmd.io/@teshenglin/2025_ML_grading)

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* [Assignment-0: GitHub 入門練習](https://hackmd.io/@teshenglin/2025_ML_week_0_AS)

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## Course calander:

| 週三 | | 週五 | |
|------|---|------|--------|
| 9/3 | Supervised learning, [notes](https://hackmd.io/@teshenglin/2025_ML_week_1), [Assignment~1](https://hackmd.io/@teshenglin/2025_ML_week_1_AS) | 9/5 | Fully-connected neural network and backpropogation |
| 9/10 | MLE, MSE, LWLR, [notes](https://hackmd.io/@teshenglin/2025_ML_week_2), [Assignment~2](https://hackmd.io/@teshenglin/2025_ML_week_2_AS) | 9/12 | Function approximation |
| 9/17 | function approximation, [notes](https://hackmd.io/@teshenglin/2025_ML_week_3), [Assignment~3](https://hackmd.io/@teshenglin/2025_ML_week_3_AS) | 9/19 | classification loss |
| 9/24 | Logistic regression, [notes](https://hackmd.io/@teshenglin/2025_ML_week_4), [Assignment~4](https://hackmd.io/@teshenglin/2025_ML_week_4_AS) | 9/26 | Gaussian discriminant analysis |
| 10/1 | Gaussian discriminant analysis, [notes](https://hackmd.io/@teshenglin/2025_ML_week_5), [Assignment~5](https://hackmd.io/@teshenglin/2025_ML_week_5_AS) | 10/3 | Generalized linear model |
| 10/8 | [notes](https://hackmd.io/@teshenglin/2025_ML_week_6), [Assignment~6](https://hackmd.io/@teshenglin/2025_ML_week_6_AS) | 10/10 | **Holiday** |
| 10/15 | Score matching, [notes](https://hackmd.io/@teshenglin/2025_ML_week_7), [Assignment~7](https://hackmd.io/@teshenglin/2025_ML_week_7_AS) | 10/17 | Sliced score matching, [notes](https://hackmd.io/@teshenglin/2025_ML_week_7_2) |
| 10/22 | SDE, [notes](https://hackmd.io/@teshenglin/2025_ML_week_8), [Assignment~8](https://hackmd.io/@teshenglin/2025_ML_week_8_AS) | 10/24 | **Holiday** |
| 10/29 | Ito's lemma and Fokker Planck equation [notes](https://hackmd.io/@teshenglin/2025_ML_week_9), [Assignment~9](https://hackmd.io/@teshenglin/2025_ML_week_9_AS) | 10/31 | PF-ODE |
| 11/5 | Reverse SDE, [notes](https://hackmd.io/@teshenglin/2025_ML_week_10), [Assignment~10](https://hackmd.io/@teshenglin/2025_ML_week_10_AS) | 11/7 | score PDE |
| 11/12 | Physics-informed neural networks, [notes](https://hackmd.io/@teshenglin/2025_ML_week_11), [Assignment~11](https://hackmd.io/@teshenglin/2025_ML_week_11_AS) | 11/14 | Operator learning |
| 11/19 | **No lecture** | 11/21 | least square regression and its application |
| 11/26 | Neural ODE | 11/28 | PCA |
| 12/3 | LLE/MDS | 12/5 | Diffusion maps |
| 12/10 | Final project presentation~1 | 12/12 | Final project presentation~2 |
| 12/17 | Final project presentation~3 | 12/19 | **No lecture** |

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