機器學習

Machine Learning

學期

113-1

學分

3 學分

當期課號

639404

永久課號

AIIT30005

開課單位

智慧計算與科技研究所智慧物聯網產業碩士專班

授課教師

馬清文

校區

歸仁

類別

必修

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, Stanford cs229 machine learning summer edition, provides basic, general, and advanced machine learning concepts. Topics include supervised learning, reinforcement learning, unsupervised learning, variational inference, etc. We will also include decision trees and recent advanced topics, such as self-supervised learning, contrastive learning, and large-language models.

先修科目

Basics of probability theory, linear algebra, and multivariable calculus Reasonably computer programming skills in Matlab/Python/numpy.

教學方式

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

評分方式

5 Homework Problem sets: 1 Term project proposal: 1 Term project report = 40%:20%:40%

週次計畫

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
第 2 週	2. Gradient Boost Decision tree, XG-Boost, Tabular data vs. multi-media data
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
第 4 週	4. Perceptron Logistic Regression Newton's Method
第 5 週	Part 2. General Machine Learning Concepts5. Exponential Family Generalized Linear Models (GLM) Gaussian Discriminant Analysis (GDA) Naive Bayes Laplace Smoothing
第 6 週	6. Kernel Methods Support Vector Machine
第 7 週	7. Support Vector Machine Application Bayesian Methods (optional) Parametric (Bayesian Linear Regression, optional) Non-parametric (Gaussian process, optional)
第 8 週	8. Neural Networks and Deep Learning Problem set 3 Release
第 9 週	* Theory *9. Bias and Variance Regularization, Bayesian Interpretation Model Selection
第 10 週	10. Bias-Variance tradeoff (wrap-up) Empirical Risk Minimization Uniform Convergence
第 11 週	* Reinforcement Learning *11. Reinforcement Learning (RL) Markov Decision Processes (MDP) Value and Policy Iterations Learning MDP model Continuous States
第 12 週	12. Recap, Review, and Term project proposal
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
第 14 週	Part3. Advanced machine learning concepts14. Variational Inference EM Variants Variational Autoencoder Principal Components Analysis (PCA)
第 15 週	15. Advanced topics To be determined
第 16 週	16. Term project report

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