40717 Machine Learning

Course Description

In this course, we will introduce the field of machine learning, focusing on the core concepts of supervised, unsupervised, and reinforcement learning. In supervised learning, we learn a function mapping between the input and the output based on input data labeled with the desired output. The purpose of unsupervised learning is to discover latent structures in input samples when output labels are not available. We will discuss reinforcement learning models and algorithms when evaluative feedback is available.

Course Information

Required Texts

  1. [BSH] Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, 2006.

  2. [MIT] Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.

  3. [MUR12] Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012.

  4. [MUR22] Kevin P. Murphy, Probabilistic Machine Learning: An Introduction, The MIT Press, 2022.

  5. [SB] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Second edition. The MIT Press, 2018.

  6. [HAL] Hal Daume III, A Course in Machine Learning, 2014.

Grading Policy

  1. 25%: Mid-term exam 1 (1402/01/30).

  2. 25%: Final exam

  3. 30%: Homeworks.

  4. 10%: Quiz.

  5. 10%: Paper & Explore a theoretical or empirical question and present it. Deadline for choosing paper: 1402/01/30.

Lecture Schedule


Lecture Date Topics Related Readings and Links Homeworks & Assignments Quizes
1 1401-11-17Introduction Chapter 1 of BSH
Chapter 1 of MUR12
Chapter 1 of MUR22
2 1401-11-24 Overview of probability theory Chapter 2 of BSH
Chapter 1 of MUR22
HW1
3
4
1401-12-01
1401-12-06
Regression Chapter 3 of BSH
Chapter 11 of MUR22

HW2
Quiz 1
5 1401-12-08 Decision trees Chapter 3 of MIT
6 1401-12-13 Instance-based learning Chapter 8 of MIT
7 1401-12-15 Hypothesis Evaluation Chapter 5 of MIT
8
9
10
1401-12-20
1401-12-22
1402-01-14
Probabilistic Classifiers Sections 1.5, 2.3, 2.5, & 4.2 of BSH
Chapter 5 of MUR22
HW3 Quiz 2
11
12
13
1402-01-19
1402-01-21
1402-01-26
Logistic Regression
Support Vector Machines
Sections 4.1.2, 4.3.2, 7.1, 14.2 of BSH
Chapters 10 & 17 of MUR22

HW4

Quiz 3
14 1402-01-28 Multi-class Classification Section 4.1.2 of BSH
Chapter 6 of HAL
1402-01-30 Mid-term exam 11:00-13:00, CE 102
15 1402-02-04 Ensemble Learning Section 14.2 of BSH
16 1402-02-09 Computational Learning Theory Chapter 7 of MIT HW5 Quiz 4
17
18
19
20
1402-02-11
1402-02-16
1402-02-18
1402-02-23
Dimensionality Reduction Section 12.1 of BSH
Chapter 20 of MUR22
HW6
21
22
1402-02-25
1402-02-30
Clustering Chapter 9 of BSH
Chapter 20 of MUR22
Chapters 1-6 of SB
Quiz 5
23
24
25
1402-03-01
1402-03-06
1402-03-08
Reinforcement Learning Chapters 1-6 of SB HW6 Quiz 5
26 1402-03-13 Advanced Topics Chapters 1-6 of SB Quiz 5