機器學習
Machine Learning
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 機器學習 CM215（歸仁） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
This course teaches basic, general, and advanced machine learning concepts. Basic concepts such as linear regression, decision trees, supervised learning, neural networks, cross-validation, etc. Common concepts such as unsupervised learning, reinforcement learning, deep neural networks, error analysis, etc. Advanced concepts include variational inference and diffusion models.
Basics of probability theory, linear algebra, and multivariable calculus Reasonably computer programming skills in Matlab/Python/numpy.
無備註
Reference web sites: https://cs229.stanford.edu/syllabus-summer2020.html https://github.com/maxim5/cs229-2018-autumn https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF
5 Homework Problem sets: 1 Term project proposal: 1 Term project report = 40%:20%:40%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Part 1. Basic Machine learning concepts 1. Course outline, Introduction and Logistics K-near neighbors, Decision tree, Random forest 2024-09-03(二) 時數：[2024-09-03]馬清文(3.00) |
| 第 2 週 | 2. Gradient Boost Decision tree, XG-Boost, Tabular data vs. multi-media data 2024-09-10(二) 時數：[2024-09-10]馬清文(3.00) |
| 第 3 週 | *** Supervised Learning *** 3. Linear Regression [Stochastic] Gradient Descent ([S]GD) Normal Equations Probabilistic Interpretation Maximum Likelihood Estimation (MLE) Problem Set 1 and 2 Release 2024-09-17(二) 時數：[2024-09-17]馬清文(3.00) |
| 第 4 週 | 4. Perceptron Logistic Regression Newton's Method 2024-09-24(二) 時數：[2024-09-24]馬清文(3.00) |
| 第 5 週 | Part 2. General Machine Learning Concepts 5. Exponential Family Generalized Linear Models (GLM) Gaussian Discriminant Analysis (GDA) Naive Bayes Laplace Smoothing 2024-10-01(二) 時數：[2024-10-01]馬清文(3.00) |
| 第 6 週 | 6. Kernel Methods Support Vector Machine 2024-10-08(二) 時數：[2024-10-08]馬清文(3.00) |
| 第 7 週 | 7. Support Vector Machine Application Bayesian Methods (optional) Parametric (Bayesian Linear Regression, optional) Non-parametric (Gaussian process, optional) 2024-10-15(二) 時數：[2024-10-15]馬清文(3.00) |
| 第 8 週 | 8. Neural Networks and Deep Learning Problem set 3 Release 2024-10-22(二) 時數：[2024-10-22]馬清文(3.00) |
| 第 9 週 | *** Theory *** 9. Bias and Variance Regularization, Bayesian Interpretation Model Selection 2024-10-29(二) 時數：[2024-10-29]馬清文(3.00) |
| 第 10 週 | 10. Bias-Variance tradeoff (wrap-up) Empirical Risk Minimization Uniform Convergence 2024-11-05(二) 時數：[2024-11-05]馬清文(3.00) |
| 第 11 週 | *** Reinforcement Learning *** 11. Reinforcement Learning (RL) Markov Decision Processes (MDP) Value and Policy Iterations Learning MDP model Continuous States 2024-11-12(二) 時數：[2024-11-12]馬清文(3.00) |
| 第 12 週 | 12. Recap, Review, and Term project proposal 2024-11-19(二) |
| 第 13 週 | *** unsupervised learning *** 13. K-means clustering Mixture of Gaussians (GMM) Expectation Maximization (EM) Principal Components Analysis (PCA) Independent Components Analysis (ICA) Problem set 4.1 4.3 release 2024-11-26(二) 時數：[2024-11-26]馬清文(3.00) |
| 第 14 週 | Part3. Advanced machine learning concepts 14. Variational Inference EM Variants Variational Autoencoder Principal Components Analysis (PCA) 2024-12-03(二) 時數：[2024-12-03]馬清文(3.00) |
| 第 15 週 | 15. Advanced topics To be determined 2024-12-10(二) 時數：[2024-12-10]馬清文(3.00) |
| 第 16 週 | 16. Term project report 2024-12-17(二) 時數：[2024-12-17]馬清文(3.00) |
https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf
- 地點
- online
- 時間
- on appointment
- 聯絡方式
- email: machingwen@ncyu.edu.tw