機器學習

Machine Learning

學期

113-1

學分

3 學分

當期課號

639007

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	週二
5 13:20–14:10	機器學習 CM215 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 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.

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 Concepts 5. 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 concepts 14. 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