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课程详述

COURSE SPECIFICATION

以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请

联系授课教师。

The course information as follows may be subject to change, either during the session because of unforeseen

circumstances, or following review of the course at the end of the session. Queries about the course should be

directed to the course instructor.

1. 课程名称 Course Title

课程编号

Course Code

BMEB215

4. 课程学分 Credit Value

3

5.

课程类别

Course Type

专业基础课 Major Foundational Courses

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授课教师、所属学系、联系方

式（如属团队授课，请列明其

他授课教师）

Instructor(s), Affiliation&

Contact

（For team teaching, please list

all instructors）

刘泉影，生物医学工程系

liuqy@sustech.edu.cn

选课人数限额(可不填)

Maximum Enrolment

（Optional）

2

11.

授课方式

Delivery Method

学时数

13.

is a pre-requisite

14.

The goal of this course is understanding a large number of learning algorithms in machine learning and knowing how to

evaluate the learning algorithm and how to select models. Implementing in code common machine learning algorithms.

Machine learning mainly includes linear model, support vector machine, kernel method, artificial neural network,

clustering and dimension reduction, etc. topics. In addition, this course will also introduce the application of machine

learning in medical image segmentation, registration, prediction, classification, etc. fields. Training students' ability to

solve practical medical problems by machine learning. Students will complete a course research in the form of a group.

16.

预达学习成果 Learning Outcomes

通过学习，本课程预达下列学习成果：

1. 对机器学习有基础的认识

2. Master commonly used machine learning algorithms

3. Knowing applications of machine learning in medicine

4. In the form of group, study a specific medical application problem, read related research literatures, and try to

use machine learning methods to solve this problem. Show the group research results and complete the

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course group report.

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课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明

主讲人）

Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if

this is a team teaching or module course.)

Section 2 Model Evaluation and Selection

Learning the concepts of precision, error, overfitting, and understanding various ways of evaluating models。

Section 3 参数估计技术基础（3 学时）

介绍基本的参数估计技术，包括最大似然参数估计、贝叶斯参数估计

Section 3 Basic Parameter Estimation Techniques

Introduction of basic parameter estimation techniques.

Section 4 线性模型（3 学时）

介绍经典的线性模型及线性回归。

Section 4 Linear Model

Introduction of linear model and linear regression.

Section 6 SVMs and Kernel Methods

Introduction of SVMs and kernel methods.

Section 7 集成学习（3 学时）

介绍集成学习概念、Boosting、Bagging 及随机森林算法。

Section 7 Ensemble Learning

Introduction of concept of ensemble learning, Boosting, Bagging and random forest algorithms.

Section 8 聚类（3 学时）

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介绍聚类及常见的聚类算法。

Section 8 Clustering

Introduction of clustering and common clustering algorithms.

Section 10 Graphical Models

Introduction of probabilistic graphical models and the applications

介绍机器学习在医学应用的发展过程及其在各个领域所做出的贡献。

Section 11 Introduction of Medical Application

Introduce the development process of machine learning in medical application and its contribution in various fields.

Section 12 医学图像重建及配准（3 学时）

介绍机器学习在医学图像重建及配准中的应用。

Section 12 Medical Images Reconstruction and Registration

Application of machine learning in medical images reconstruction and registration.

Section 13 医学图像分割及形状检测（3 学时）

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Textbook

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