機器學習

Machine Learning

學期

111-1

學分

3 學分

當期課號

535373

永久課號

EECM30060

開課單位

電信工程研究所

授課教師

簡仁宗

校區

光復

類別

選修

上課時間表

節	週五
5 13:20–14:10	機器學習 EDB26 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

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data from sensor data or databases. A major focus is to automatically learn to recognize complex patterns and make intelligent decisions based on data. This course shall deliver fundamental theories of machine learning which can be applied for many intelligent information systems.

先修科目

Calculus, Linear Algebra, Probability & Statistics

教學方式

Lecture notes will be provided. Teacher assistants (楊立任, 王心玓, 葉宜萍, 徐靖憲) are available at PM 19:00-20:00 in ED 708 in week days. Any questions about ML and homework are welcome. Appointments are required.

評分方式

Temporary Grading Policy: Midterm Exam (30%), Final Exam (35%), Homework (35%)

課程大綱

週次計畫

週次	主題
第 1 週	Introduction to Machine Learning
第 2 週	Curve Fitting & Model Selection & Decision Theory
第 3 週	Information Theory & Probability Functions
第 4 週	Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian & Student t Distributions
第 5 週	Generative Models - Least Squares & Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori
第 6 週	Bayesian Linear Regression, Bayesian Model Comparison & The Evidence Framework
第 7 週	Discriminant Function - Least Squares, Fisher's Discriminant & Perceptron Algorithm
第 8 週	Discriminative Model - Logistic Regression, Laplace Approximation & Bayesian Logistic Regression
第 9 週	Kernel Methods & Gaussian Process
第 10 週	Midterm Exam
第 11 週	Sparse Kernel Methods - Large Margin Classifier
第 12 週	Support Vector Machine & Relevance Vector Machine
第 13 週	Mixture Models and EM
第 14 週	Hidden Markov Models
第 15 週	Approximate Inference
第 16 週	Final Exam

教科書

1. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. 2. S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing, Cambridge University Press, 2015. 3. J.-T. Chien, Source Separation and Machine Learning, Academic Press, 2018. 4. M.-W. Mak and J.-T. Chien, Machine Learning for Speaker Recognition, Cambridge University Press, 2020.

Office Hours

地點: ED708
時間: PM17:30-PM18:30 on Monday (with appointment)
聯絡方式: jtchien@nycu.edu.tw