Semester: 4, 2

Credit: 2

Program: Computer Science

Course Module: Specialized Optional

Responsible:Bo Wang

E-mail:bo_wang@tju.edu.cn

Department: College of Intelligence and Computing, Tianjin University

Time Allocation (1 credit hour = 45 minutes)

Exercise

Lecture

Lab-study

Project

Internship (days)

Personal Work

8

16

24

8

Prerequisite

Ÿ Probability and Mathematical Statistics: master the mathematical foundation of machine learning.

Artificial Intelligence: understand the field of artificial intelligence, machine learning is the core of artificial intelligence.

Course Objectives

This course discusses the concept of machine learning to help students understand the field of artificial intelligence better and enhance their professional skills. After this course, students should be able to:

Ÿ Comprehensively master and understand machine learning research, the present situation and development of its application field, and tomaster the mainstream learning methods and models, choose and implement the corresponding algorithm according to the practical problems.

CourseSyllabus

Ÿ Decision tree learning: master the basic knowledge of decision theory and information theory.

Ÿ Supervised learning and unsupervised learning: the concept of supervised learning and unsupervised learning.

Ÿ Evaluating hypotheses: common distribution and the method of maximum likelihood estimation.

Ÿ Linear regression model: the basic methods of linear regression.

Ÿ SVM: the basic principle of support vector machine.

Ÿ Graph model: from modelling to the implementation of algorithm.

Ÿ Expectation Maximization algorithm: the basic theory of EM algorithm and the use of EM algorithm.

Hidden Markov Model and conditional random field model: a few classic algorithms of Hidden Markov Model.

Textbooks & References

Ÿ Christopher M. Bishop.Pattern Recognition and Machine Learning. Springer, 2007.

Ÿ Tom M. Mitchell.Machine Learning. McGraw-Hill Science/Engineering/Math, 1997.

Kevin P. Murphy. Machine Learning. The MIT Press, 2012.

Capability Tasks

CT1: To understand basic science, and to have analytical ability and the ability to integrate related knowledge.

CT2: To apply relevant professional knowledge to the field of science and technology: understanding of the basic concepts and its connotation, application of different methods and concepts which have been learned, capability of judging the scope and limitations of such applications.

CT3: To grasp methodologies and engineering tools: identifying, utilizing and solving problems. Even if the students are not familiar with the content, they can turn to computer tools for systematic analysis.

CT4: To carry out experiments in research environment with the abilities to utilize tools, especially for data collection and processing.

CT6: To understand the nature of a given task, such as professional ethnics, morals, security and health management.

CT8: To understand social demands.

CS1: Master the basic theoretical knowledge of own major and understand the development status and trend of own major.

CS2: Firmly master the core knowledge and relevant engineering knowledge for computer major, and possess the primary capability of applying the core knowledge and engineering knowledge to system development.

CS3：Able to possess relatively good system analysis and software design capabilities, master advanced technologies for solving the actual project problems, able to participate in formulating actual engineering solutions, participate in formulating implementation plans, implement project solutions and complete project tasks, and able to undertake engineering or management work, and effectively solve actual engineering problems in the field of computer science.

CS4: Familiarly master and use professional technical language, able to edit engineering documents and carry out technical exchange, able to follow the latest technical development trend in the field, and possess team cooperation spirit and effective negotiation and communication capabilities.

Achievements

Ÿ To be familiar with the basic knowledge of decision tree and information theory. - Level: A

Ÿ To be familiar with the common distribution. - Level: A

Ÿ To master the way of linear regression, and to learn how to use regression software package in R, and how to solve practical problems. - Level: M

Ÿ To have a full understanding of classification problem, and to master some commonly classification methods. - Level: M

Ÿ To master the basic principle of SVM, and to learn to use SVM-to solve specific problems in their respective fields. - Level: M

Ÿ To master the process from modelling to algorithm implementation. - Level: M

Ÿ To master the basic theory of the EM-algorithm, and to learn how to use the EM-algorithm. - Level: M

To master classic algorithms of hidden Markov model, and to learn to use hidden Markov model and conditional random field model to solve specific problems. - Level: M

Students:Computer Science, Year 2

Assessment：

Exam

Assignment

Report

Term Paper

Presentation

Others

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Language of assessment：English/Chinese

Attendance： % Homework: 30 %

Mid-term report/test： % Final report/test： 70 %