VE445: Introduction to Machine Learning

Shanghai Jiao Tong University, Autumn 2019

Teaching Staff

	name	email	office hour
Instructor	Shuai Li	shuaili8@sjtu.edu.cn	Tue 10:00-11:00 Rm 1406-2, Software College
TA	Jingying Wang	wjymonica@sjtu.edu.cn	Mon 20:00-21:00 326G, JI Building

News

• Lab 3 is on Nov 27 8:00-9:40 at Dong Xia Yuan 204 and is about EM and GCN. [pdf]
• Guest Lecture is on Recommendation Systems. [abstract][slides]
• Lab 2 is about the R-CNN variants and autoencoder. [pdf]
• Lab 1 is on tensorflow and the project. [pdf][tensorflow tutorial][demo]

Course Information

Lecture times

• Tue 8:00-9:40 at Dong Xia Yuan 113
• Thu 8:00-9:40 at Chen Rui Qiu Building 102
• Fri 8:00-9:40 at Dong Xia Yuan 102 (every odd week)

Recitation time Mon 18:00-19:40 at F103

Grading Your grade will be determined from a final exam (35%), a midterm exam (25%), a project (20%), and labs/homeworks (20%).

References

• Machine Learning : A Probabilistic Perspective by Kevin P. Murphy
• Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
• CS420 Machine Learning taught by Weinan Zhang
• CS229 Machine Learning by Stanford
• CS231n: Convolutional Neural Networks for Visual Recognition by Stanford
• CS246: Massive Data Mining by Stanford

Schedule

week	date	topic	materials
1	Sep 10	1 Introduction	pdf
	Sep 12	2 Basics	pdf
2	Sep 17	3 Linear Regression	pdf
3	Sep 19 Sep 24	4 Logistic Regression	pdf
	Sep 26	5 SVM	pdf
	Sep 27	Practice: Python
4	Oct 1 Oct 3	National Day Holiday
5	Oct 8	5 SVM (cont.)
	Oct 10 Oct 11	6 Neural Networks	pdf demo
6	Oct 15 Oct 17	7 Convolutional Neural Networks	pdf
7	Oct 22	8 Recurrent Neural Networks	pdf demo
	Oct 24	9 Decision Tree	pdf
	Oct 25	Review for the Midterm	pdf
8	Oct 29	Midterm Exam
	Oct 31	9 Decision Tree (cont.)
9	Nov 5	China International Import Expo
	Nov 7 Nov 8	10 Clustering	pdf
10	Nov 9 Nov 12	11 Dimensionality Reduction	pdf
	Nov 14	Guest Lecture: Recommendation Systems
11	Nov 19 Nov 21	11 Dimensionality Reduction (cont.)
12	Nov 22 Nov 26	12 Gaussian Mixture Models	pdf
	Nov 28	13 Hidden Markov Models	pdf
13	Dec 3	HMM, Review for the Final
	Dec 5	Review for the Final	pdf
	Dec 6	Final Exam