Course Introduction to Machine Learning

milkygoodyearAI and Robotics

Oct 14, 2013 (3 years and 8 months ago)

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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