Course Introduction to Machine Learning

Semester 1

Instructor TTIC Faculty and Prof. Yutaka Sasaki

Credit 2

Course Description

This course covers a variety of the latest topics from machine learning and computer vision, and provides necessary knowledge

for students to understand the latest progress in these research areas. All lectures are basically given by faculty of TTI at

Chicago through a video conference system.

Grading

Final Exam : 40%, Homework: 60%

Preparations for class and Prerequisites

English conversation ability (especially listening and reading) is requested.

Prerequisites : “Probability and Statics” , “Linear algebra 2” , “Academic English 1−4”

and “Science and Engineering English 1−4”

Textbook

Christopher M. Bishop: "Pattern recognition and machine learning" (Springer 1st edition, 2006) ISBN978-0-387-31073-2

Reference book

None

Schedule and Contents

Chapter

Contents

Pages

1 Preparation

Introduction and motivation: goals of machine

learning, applications

2 Chap. 1 Probability theory pp. 12-32

3 Chap. 1 Statistical problem setup; Loss and risk pp. 1-58

4 Chap. 4 Linear regression and least squares pp. 179-185

5 Chap. 2, 3 Maximum likelihood estimation and noise models pp. 137-143

6 Chap. 3 Bias-variance tradeoff pp. 147-152

7 Chap. 1, 3 Model complexity and regularization pp. 10-11, pp. 161-165

8 Chap. 1, Regularization and MAP estimation pp. 28-30

9 Chap. 1 Introduction to classification; Decision theory pp. 38-48

10 Chap. 4 Logistic regression pp. 203-207

11 Chap.1 Generative models; Naive Bayes pp. 42-46

12 Chap. 9 Multivariate Gaussians; Mixture models and EM pp. 423-430

13 Chap. 9 Mixture models and EM pp. 430-435

14 Chap. 2 Nonparametric methods pp. 120-127

15 Chap. 7 Support Vector Machines pp. 325-345

16 Final Exam

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