Leader of Teaching Group: Xia Tao

Teacher: Xia Tao

Semester: Spring Term

Courses and reference books: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

Discipline: Computer Science and Technology

Course number: 0828356

Credits: 3.0

Course Type: Undergraduate Course:

Required Class Hours: 48.0

Top-Quality Courses or Not: no

Maximum Number of Students: 213

Members of Teaching Group: Xia Tao

Course Introduction: Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security, from analyzing biochemical interactions to structural monitoring of aging bridges, etc.

Venue: Huazhong Univ. of Sci.&Tech.

Testing Method: Team project review

Schedule: May 26, 2020 - June 19, 2020

Date of Examination: June 26, 2020

Teaching Plan: Knowledge and Understanding Based on fundamental knowledge of computer science principles and skills, probability and statistics theory, and the theory and application of linear algebra. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: (1) supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, and support vector machines); (2) unsupervised learning (clustering, dimensionality reduction, kernel methods); (3) learning theory (bias/variance tradeoffs; VC theory; large margins); and (4) reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. By the end of the course, students should be able to: * Develop an appreciation for what is involved in learning models from data. * Understand a wide variety of learning algorithms. * Understand how to evaluate models generated from data. * Apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.

Class classroom: West #12 S510

School Year: 2019-2020