MS004111-机器学习与进化计算

研究生课程开设申请表

 开课院（系、所）：信息科学与工程学院       

 课程申请开设类型： 新开□     重开√     更名□（请在□内打勾，下同）

一、课程介绍（含教学目标、教学要求等）（300字以内）

本课程教学的目标是使学生掌握多种机器学习范型、算法及进化计算在机器学习领域的研究和应用，吸取包括概念学习、决策树学习、人工神经网络知识、统计和估计理论、贝叶斯观点、计算学习理论、基于实例的学习方法、进化计算理论、学习规则集合的算法、分析学习、归纳与分析学习相结合以及增强学习方面的研究成果。要求通过本课程的教学，明确机器学习系统的几个重要环节：选择训练经验、目标函数、目标函数的表示、函数逼近算法，从而提高学生设计学习系统的能力， 增强学生对于机器学习这个多学科领域分析问题和解决问题的能力。

二、教学大纲（含章节目录）：（可附页）

（一）机器学习的概念（第一章）

包括学习问题的标准描述，设计学习系统的主要环节，机器学习中的经典问题和观点。

（二）概念学习（第二章）

包括基于符号和逻辑表示的概念学习，假设的一般到特殊偏序结构和学习中引入归纳偏置的必要性。

（三）决策树学习、人工神经网络知识（第三章、第四章）

包括决策树学习和过度拟合训练数据的问题、人工神经网络中的反向传播算法以及梯度下降的一般方法。

（四）评估假设（第五章）

包括统计和估计理论的基础概念，使用有限的样本数据评估假设的精度。

（五）贝叶斯学习（第六章）

包括使用贝叶斯分析刻画非贝叶斯学习算法以及直接处理概率的贝叶斯算法。

（六）计算学习理论（第七章）

包括可能近似正确学习模型和出错界限学习模型及联合多个学习方法的加权多数算法。

（七）基于实例的学习（第八章）

包括k-近邻算法、局部加权回归、径向基函数以及基于案例的推理算法。

（八）进化计算（第九章）

包括进化计算的搜索策略和实现方法。

（九）学习规则集合（第十章）

包括序列覆盖算法以及学习一阶规则集方法理论。

（十）分析学习、归纳和分析学习的结合（第十一、十二章）

包括纯粹的分析学习方法（基于解释的学习）、结合分析和归纳学习以提高学习精度的方法。

（十一）增强学习（第十三章）

包括解决自治agent学习控制策略问题、马尔可夫决策过程问题以及Q学习。

三、教学周历

四、主讲教师简介：

徐琴珍，女，1977 年生，讲师，博士，研究方向包括智能信息处理，超声图像处理，混合学习方法研究。主持一项国家自然科学基金项目，研究成果以论文形式发表于国际期刊、国内核心期刊以及国际会议上。

五、任课教师信息（包括主讲教师）：

Application Form For Opening Graduate Courses

School (Department/Institute)：School of Information Science and Engineering

Course Type: New Open □   Reopen√   Rename □（Please tick in □, the same below）

1. Course Introduction (including teaching goals and requirements) within 300 words:

This course enables the students to comprehend a variety of learning paradigms, algorithms and the research and applications of evolutionary computing in the field of machine learning, and draw on the research results covering concept learning, decision tree learning, artificial neural network knowledge, statistics and estimation theory, Bayesian perspective, computational learning theory, instance-based learning methods, evolutionary computation theory, algorithms for learning sets of rules, analytical learning, combining inductive and analytical learning, and reinforcement learning. The course takes up with clarifying the important links of machine learning involving choosing the training experiences, choosing the target function, choosing a representation for the target function, and choosing a function approximation algorithm. Students who complete this course will have demonstrated the enhanced ability to design a learning system, analyzing problems, and solve problems in the multidisciplinary machine learning field.

2. Teaching Syllabus (including the content of chapters and sections. A sheet can be attached):

(1) The Concept of Machine Learning (Chapter 1)

Including well-posed learning problems, the main links of designing a learning system, and some classic problems and viewpoints in machine learning.

(2)Concept Learning (Chapter 2)

Covering concept learning based on symbolic and logic representation, the general-to-specific ordering over hypotheses, and the need for inductive bias in learning.

(3)Decision Tree and Artificial Neural Network(Chapter 3, 4)

Presenting decision tree learning, the problems of overfitting the training data, the backpropagation algorithm and the general approach of gradient decent for neural network training.

(4)Evaluation Hypotheses(Chapter 5)

Including basic concepts from statistics and estimation theory, evaluating the accuracy of hypotheses using limited samples of data.

(5)Bayesian Learning (Chapter 6)

Including the use of Bayesian analysis to characterize non-Bayesian learning algorithms and specific Bayesian algorithms that explicitly manipulate probabilities.

(6)Computational Learning Theory (Chapter 7)

Covering the Probably Approximately correct learning model, the Mistake-Bound learning model and a discussion of the weighted majority algorithm for combining multiple learning methods.

(7)Instance-based Learning (Chapter 8)

Including k-nearest neighbor learning, locally weighted regression, radial basis function, and case-based reasoning.

(8)Evolutionary Computing (Chapter 9)

Covering the searching strategies of evolutionary computing and implementation methods.

(9)Learning Set of Rules(Chapter 10)

Presenting the theory of sequential covering algorithm and approaches to learning sets of first-order rules.

(10)Analytical Learning, and Combing of Inductive and Analytical Learning

Including purely analytical learning (explanation-based learning), andcombining inductive and analytical learning to improve the accuracy of learned hypotheses.

(11)Reinforcement Learning

Addressing the problems of learning control strategies for autonomous agents, Markov decision process, and Q learning.

3. Teaching Schedule:

Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.

2. Course terms: Spring, Autumn , and Spring-Autumn term.  

3. The teaching languages for courses: Chinese, English or Chinese-English.

4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline.

5. Practice includes: experiment, investigation, research report, etc.

6. Teaching methods: lecture, seminar, practice, etc.

7. Examination for degree courses must be in paper.

8. Teaching material websites are those which have already been announced.

9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree achieved, professional title), research direction, teaching and research achievements. (within 100-500 words)

4. Brief Introduction of Chief lecturer:

Qinzhen Xu was born in 1977. She received PhD degree from Signal and Information Processing Major of School of Information Science and Engineering of Southeast University in 2007. She is now an instructor of school of Information Science and Engineering of Southeast University. Her research interests include intelligent information processing, ultrasonic image processing, hybrid learning model. Her research results have been published in international journals, inland journals, and international proceedings.

Luxi Yang was born in 1964. He is now a professor and the director of Digital Signal Processing Division in School of Information Science and Engineering of Southeast University. His major research interests include communication signal processing, MIMO communication system designing, blind signal processing, and space-time signal processing for mobile communications. He has published over 100 journal papers, applied 8 patents for invention and received the first- and second-class prizes of Science and Technology Progress Awards of the State Education Ministry of China for 3 times, and the first-class prizes of Science and Technology Progress Awards of Jiang-su Province of China for 2 times.

5. Lecturer Information (include chief lecturer)