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Introduction

Course Description:
Machine Learning and Data Mining is a research discipline in attempting to search through data for hidden relationships and patterns and worthy information using machine learning methods. This course is to offer an introduction to Machine Learning and Data Mining covering from mechanism, models, algorithm to some typical Machine Learning and Data Mining applications as well. The course Machine Learning and Data Mining covers the following interesting topics: a brief history of Machine Learning and Data Mining, research and philosophical questions faced by AI practitioners, representing and solving AI problems in a state space search formalism, heuristics, connectionism, and specific machine learning problems such as vision and natural language.

Course Objectives:
Through this course, students will understand the basic concepts of machine learning and data mining, master the basic principles and methods of machine learning and data mining, and inspire students' interest in machine learning and data mining, cultivation of knowledge and technological innovation capacity. Students applying machine learning and data mining techniques to improve the ability to analyse and solve complex problems.

Prerequisites:
Advanced Programming, Mathematical Logic

Evaluation:
Students will be graded on their understanding of the course as reflected in their performance on the homework, class participation, examinations, and projects as follows (approximately):

Homework 20% + Projects 40% + Final Exams 40%

Instructor:
Liqing Zhang, Prof.
Office: Room 3-435, SEIEE Building
Tel: 34204423
Email: zhang-lq@cs.sjtu.edu.cn


Bo Yuan, Ph.D., Prof.
Office: Room 3-401, SEIEE Building
Phone: 131-6211-5543
Email: boyuan@sjtu.edu.cn

Textbook and References:

Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Englewood Cliffs, New Jersey, 2003.

References:
[1] George Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Addison-Wesley, 2008
[2] Nils Nilsson, "Artificial Intelligence", Morgan Kaufman, 1998

Homework

Deal with the issues related to your homework.

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Slide
Ch1-Introduction
Ch3-UninformedSearch
Ch2-IntellAgents
Ch4-InformedSearch
Ch5-CSP
Ch4-MachineEvolution
Ch6-AdversarialSearch
Ch7-Logical Agents
Ch8-FirstOrderLogic
Ch9-Inference-FOL
Ch10-KowledgeBase
Ch13-Uncertainty
CH14-Inference Uncertainty
Ch15-Inference-Time
Ch10KnowledgeBase-New
Ch16-Decisions
Ch18-Learning

Prof. Yuan's Slides

Lecture 01
Lecture 02
Lecture 03
Lecture 04
Lecture 05
Lecture 06
Lecture 07
Lecture 08
Lecture 09
Final exam example

Paper
Overview Paper-Computer Go

Other