课程名称 (Course Name) ： Machine Learning

课程代码 (Course Code）： X033525

学分/学时 (Credits/Credit Hours)： 3/(48h)

开课时间 (Course Term )：  Friday 12：55~3：40 pm

开课学院（School Providing the Course）:  School of Electronic Information and Electrical Engineering

任课教师（Teacher）:  Yang Yang

课程讨论时数（Course Discussion Hours）: 6 (project presentation)

课程实验数（Lab Hours）: 30 (off-class)

课程内容简介(Course Introduction):

This course is designed to provide a broad introduction of the theories, algorithms and applications of machine learning. The topics include: supervised learning (generative/discriminative learning, support vector machines, logistic regressions), unsupervised learning (clustering methods), learning theory (bias/variance tradeoff, model selection, VC theory), probabilistic graphical models (HMM, structure learning) and some applications in text categorization, computational biology, image processing, etc.

教学大纲(Course Teaching Outline):

1.    Introduction (2h)

1.1         Basic concepts of Machine Learning

1.2         Machine Learning tasks

1.3         Notation of learning problems

2.    Supervised learning (16h)

2.1         Linear regression

2.2         Logistic regression

2.3         Naive Bayes

2.4         Support vector machine

3.    Unsupervised learning (12h)

3.1         Clustering methods

3.2         K-means clustering

3.3         Expectation maximization

4.    Dimension reduction (3h)

4.1         PCA

4.2         ICA

4.3         Factor analysis

5.    Learning Theory (3h)

5.1         Bias/variance tradeoff

5.2         Regularization

5.3         Model selection

5.4         VC theory

6.    Graphical models (3h)

6.1         HMM

6.2         Structure learning

7.    Machine learning applications (3h)

课程进度计划(Course Schedule):

Lecture 1: An Introduction to machine learning and linear regression

Lecture 2: Logistic regression

Lecture 3-5: Bayes classifier and naive Bayes model

Lecture 6-7: Support vector machines

Lecture 8: Learning theory

Lecture 9: Regularization and model selection

Lecture 10-11: Clustering, EM

Lecture 12: Dimension reduction

Lecture 13: Graphical model

Lecture 14: Machine learning applications

Lecture 15: Review

课程考核要求(Course Assessment Requirements)：

Final Paper Examination: 40%

Group Presentation and Report: 30%

Homework: 20%

Participation: 10%

参考文献(Course References)：

Pattern Recognition and Machine Learning. Christopher Bishop, Springer, 2006.

Machine Learning. Tom Mitchell, McGraw-Hill, 1997.

预修课程（Prerequisite Course）

Basic probability theory

Basic linear algebra

Programming language