機器學習

Machine Learning

學期

111-1

學分

3 學分

當期課號

639005

永久課號

AICA30009

開課單位

智慧科學暨綠能學院

授課教師

馬清文

校區

歸仁

類別

選修

上課時間表

節	週三
5 13:20–14:10	機器學習 CM216 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

概述

This course, with the assistance of a well-known free online video (https://cs229.stanford.edu/syllabus-summer2020.html), provides basic, general, and advanced concepts of machine learning. Topics include supervised learning, reinforcement learning, unsupervised learning, variational inference, etc. It also includes some recent advanced topics, such as self-supervised learning and contrastive learning. We use Microsoft Teams with the link https://tinyurl.com/5trweptk

先修科目

Basics of probability theory, linear algebra, and multivariable calculus Reasonably computer programming skills in Matlab/Python/numpy We use Microsoft Teams with the link https://tinyurl.com/5trweptk

教學方式

Reference web sites: https://cs229.stanford.edu/syllabus-summer2020.html https://github.com/maxim5/cs229-2018-autumn

評分方式

5 Homework Problem sets: 1 Midterm test: 1 Term project = 40%:20%:40%

週次計畫

週次	主題
第 1 週	1. Course outline, Introduction and Logistics Review of Linear Algebra Review of Matrix Calculus Problem Set 0 Release https://tinyurl.com/5trweptk
第 2 週	2. Review of Probability Review of Probability and Statistics Setting of Supervised Learning https://tinyurl.com/5trweptk
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 https://tinyurl.com/5trweptk
第 4 週	4. Perceptron Logistic Regression Newton's Method https://tinyurl.com/5trweptk
第 5 週	5. Exponential Family Generalized Linear Models (GLM) Gaussian Discriminant Analysis (GDA) Naive Bayes Laplace Smoothing https://tinyurl.com/5trweptk
第 6 週	6. Kernel Methods Support Vector Machine https://tinyurl.com/5trweptk
第 7 週	7. Support Vector Machine Application Bayesian Methods (optional) Parametric (Bayesian Linear Regression, optional) Non-parametric (Gaussian process, optional) https://tinyurl.com/5trweptk
第 8 週	8. Neural Networks and Deep Learning Problem set 3 Release https://tinyurl.com/5trweptk
第 9 週	* Theory * 9. Bias and Variance Regularization, Bayesian Interpretation Model Selection https://tinyurl.com/5trweptk
第 10 週	10. Bias-Variance tradeoff (wrap-up) Empirical Risk Minimization Uniform Convergence https://tinyurl.com/5trweptk
第 11 週	* Reinforcement Learning * 11. Reinforcement Learning (RL) Markov Decision Processes (MDP) Value and Policy Iterations Learning MDP model Continuous States Problem set 4.2 4.4 release https://tinyurl.com/5trweptk
第 12 週	12. Recap, Review, and Term project proposal https://tinyurl.com/5trweptk
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 https://tinyurl.com/5trweptk
第 14 週	14. Variational Inference EM Variants Variational Autoencoder Principal Components Analysis (PCA) https://tinyurl.com/5trweptk
第 15 週	15. Self-supervised learning Contrastive learning Supervised contrastive learning https://tinyurl.com/5trweptk
第 16 週	16. Term project report https://tinyurl.com/5trweptk

教科書

https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf

Office Hours

地點: online
時間: on appointment
聯絡方式: email: machingwen@ncyu.edu.tw