Semester: 4

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

4

12

16

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 application and tools of machine learning to help students be able to apply machine learning technologies in real problems and enhance their professional skills. After this course, students should be able to:

Ÿ Comprehensively master and understand machine learning technologies and tomaster the mainstream machine learning models and tools according to the practical problems.

CourseSyllabus

Ÿ Decision tree learning: master the application of decision theory.

Ÿ Supervised learning and unsupervised learning: master the application of supervised learning and unsupervised learning.

Ÿ Linear regression model: master the application of linear regression.

Ÿ SVM: master the application of support vector machine.

Ÿ Graph model: master the application of graph models.

Ÿ Expectation Maximization algorithm: master the application of EM algorithm.

Ÿ Deep learning: master the application of deep learning models

Ÿ Hidden Markov Model and conditional random field model:master the application 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 popular models and tools of decision tree and information theory. - Level: A

Ÿ To masterthe popular models and tools of linear regression to solve practical problems. - Level: M

Ÿ To master the popular models and tools of classification methods. - Level: M

Ÿ To master the popular models and tools of SVM. - Level: M

Ÿ To master the popular models and tools of the EM-algorithm. - Level: M

Ÿ To master the popular models and tools of hidden Markov model. - Level: M

Ÿ To master the popular models and tools of deep learning models. - 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 %