COURSE OPENING TIME: since 2009

COURSE SEMESTER: Fall semester

CREDITS/ HOURS: 2/32

PREREQUISITE COURSE: Advanced Mathematic，C programming，Probability Statistics

APPLICABLE MAJOR/ COURSE OBJECT:Information Engineering，Foreign students

I. COURSE DESCRIPTION/ OBJECTIVE/ TASK

This course is an English course for graduate students from Computer Science and other areas. It focuses on basic principles and applications in the areas of Artificial Intelligence, Computer Vision, Pattern Recognition, Artificial Intelligence, Image Processing and etc. Learning through this course, the students will understand how to apply the related knowledge and technology in actual systems, enhancing the systems and provide solution in real problems related in intelligent system.

This course aims to improve the innovation ability of undergraduate students. It mainly introduces the latest process and analysis knowledge of artificial intelligence and machine learning. This course is for students of information engineering in grade three. It adopts small class, English teaching, and the number of students in the class is limited to about 30. The teaching method will combines classroom teaching with practice, and combine textbook with academic paper.

The main task is to improve the understanding and practical ability of students in the field of intelligent information, comprehensive grasp of computer vision, pattern recognition, image processing, and improve the students' scientific research ability, practical ability and English.

II. COURSE CONTENT/ HOURS DISTRIBUTION

Chapter One，Introduction on AI and Machine Learning（2 hours）

Chapter Two，Supervised Learning Methods （6 hours Teaching/2 hours Experiment）

Chapter Three，Unsupervised Learning Methods（4 hours Teaching/2 hours Experiment）

Chapter Four，Vision Applications（4 hours Teaching/2 hours Experiment）

Chapter Five，Deep Learning（3 hours ）

Chapter Six，Machine Learning Application（2 hours）

Chapter Seven，Vision Application: Retrieval（2 hours ）

Chapter Eight，Vision Application: Recognition and Classification（1 hour）

Chapter Nine，Recent Advanced Technologies（2 hours）

III. TEACHING FORMS/ BASIC REQUIREMENTS

This course adopts small class, English teaching, and the number of students in the class is limited to about 30. The teaching method will combine classroom teaching with practice, and combine textbook with academic paper. The course will assign corresponding research task and practice, and require students to have foundation of advanced mathematical and programming ability.

IV. COURSE ASSESSMENT

Examination scores will base on the combination of common practice and final examination results. At the same time the students participate in the discussion in class will also be included in the results.

V. TEXTBOOK

[1] Artificial Intelligence and Machine Learning, Tom Mitchell, 2010, Second Edition

VI. COURSE CHARACTERISTICS

(1) Teaching takes small class, English teaching and discussion mode. Lecturer have overseas study and teaching experience. In foreign countries, lecturer have had the experience of teaching similar course. Internationalized teaching methods can improve the enthusiasm of the students and be better for students to accept the latest knowledge and methods.

(2) This course can better highlight the characteristics of scientific research, and take the teaching methods such as paper discussion, research and practice which can improve students’ research capacity and scientific research interests. It can lay the foundation for future development of students, and make students to master the latest developments in related fields.

(3) Mastering the latest technology, understanding the latest scientific research progress. Artificial intelligence, machine learning and other relevant direction are the current hot topics and most developed level in the computer science field. So it is necessary for students to learn and master this course. At present, the School of Computer Science and Engineering has opened the class Pattern Recognition, Image Processing, Computer Vision and etc. Student may have a more comprehensive understanding of artificial intelligence and related domain after studying of this course. To lay a good foundation for information students.