中文

Tao Xia

2020-09-17,Thursday

2020-09-17,Thursday

Click:

The Founding Time:..

The Last Update Time:..

·Teaching Information

Current position: 英文主页 > Teaching Research > Teaching Information
Machine Learning
Release time:2020-3-16  Hits:

Leader of Teaching Group: Xia Tao

Members of Teaching Group: Xia Tao

Venue: Huazhong Univ. of Sci.&Tech.

Schedule: May 26, 2020 - June 19, 2020

Testing Method: Team project review

Date of Examination: June 26, 2020

Teacher: Xia Tao

Class classroom: West #12 S510

Semester: Spring Term

Discipline: Computer Science and Technology

Discipline: Computer Science and Technology

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

Course number: 0828356

Credits: 3.0

Course Type: Undergraduate Course

Required Class Hours: 48.0

Top-Quality Courses or Not: no

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.

Maximum Number of Students: 213

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.