類神經網路 - 南台科技大學

kettlecatelbowcornerAI and Robotics

Nov 7, 2013 (3 years and 5 months ago)

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南台科技大學
96
學年度第一學期課程資訊

課程名稱

類神經網路

課程編碼

30M15101

系所代碼

3

開課班級

博研電子一甲


開課教師

賴培淋


學分

3.0

時數

3

上課節次地點


2 3 4
教室
E0007

必選修

選修

課程概述

The course will introduce fundamental and advanced techniques of computation
and adaptation in networks of distributed interc
onnected processing unit. Skills
from this course will be beneficial for applied and basic research in artificial
intelligence (e.

課程目標

After this lecture, you should

1. Understand the basic building blocks of artificial neural networks (ANNs)

2. Underst
and the two modes of operation in ANNs

3. Understand the importance of learning in ANNs

4. Be able to use a simple rule to create learning in an ANN

5. Begin to understand the importance of linearly inseparable problems

Know some of the problems on which A
NNs have been used


課程大綱

1. introduction to neural nets

2. Perceptrons and the LMS Algorithm

3. Backpropagation Learning

4. Visually
-
Guided Robot Control

5. Optimization Techniques

6. Overfitting, Cross
-
Validation, and Early Stopping

7. Simple Recurrent Networks

8. Language P
rocessing Models

9. Pattern Classification I

10. Pattern Classification II.

11. Radial Basis Functions.

12. The EM (Expectation
-
Maximization) Algorithm

13. Neural Networks for Control

14. Support Vector Machines

15. Time Series Prediction

16. Shared Weigh
t Networks


2

17. Competitive Learning and Kohonen Nets

18. Hebbian Learning and Principal Components Analysis

19. Hopfield Nets and Boltzmann Machines

20. Mean Field Approximation

21. Helmholtz Machines; Minimum Description Length

22. Bayesian Networks

23.
Computational Learning Theory.

24. Connectionist Symbol Processing

25. Reinforcement Learning

26. Neurophysiology for Computer Scientists


英文大綱

1. introduction to neural nets

2. Perceptrons and the LMS Algorithm

3. Backpropagation Learning

4. Visually
-
Gui
ded Robot Control

5. Optimization Techniques

6. Overfitting, Cross
-
Validation, and Early Stopping

7. Simple Recurrent Networks

8. Language Processing Models

9. Pattern Classification I

10. Pattern Classification II.

11. Radial Basis Functions.

12. The EM
(Expectation
-
Maximization) Algorithm

13. Neural Networks for Control

14. Support Vector Machines

15. Time Series Prediction

16. Shared Weight Networks

17. Competitive Learning and Kohonen Nets

18. Hebbian Learning and Principal Components Analysis

19. Hopf
ield Nets and Boltzmann Machines

20. Mean Field Approximation

21. Helmholtz Machines; Minimum Description Length

22. Bayesian Networks

23. Computational Learning Theory.

24. Connectionist Symbol Processing

25. Reinforcement Learning

26. Neurophysiology fo
r Computer Scientists


教學方式

課堂講授
,
分組討論
,
口頭報告
,
專題演講
,
實務操作
,

評量方法

作業/習題練習
,
實作評量
,
口頭報告
,
課堂討論
,
課程參與度
(
出席率
),

3

Writing the paper to publish in the conference

指定用書

Introduction to Reinforcement Learning

參考書籍


Neural Networks for Pattern Recognition (Bishop, C.M., 1995, Oxford
Universi
ty Press)


Introduction to the Theory of Neural Computation (Hertz, J. ,Krogh, A., Palmer,
R. G., 1991, Addision Wesley) (optional)


Duda, Hart and Stork, 2000, Pattern Classification, Wiley & Sons (optional)


先修科目

Basic knowledge in linear algebra, ca
lculus, and programming in C++, Matlab (or
another language), or permission by instructor

教學資源

The official course web site is at the University BlackBoard system. Course notes,
homework assignments, answers to homework, and the like will be posted here.

注意事項

Office Hours

S608
-
1, 4:30 to 5:30, Tuesday, Wednesday and Thursday, and by appointment. I'm
occasionally a few minutes late so if you show up at the stroke of 4:30 and I'm not
there, please hang out for a few minutes. My phone extension is 3140. You'
re
unlikely to reach me there except during teaching and meetings hours.


Email

Address email to me at pllai@mail.stut.edu.tw. Since I get far more email than I'm
able to respond to, please identify yourself as a student in my class and include

全程外語授課

1

授課語言
1

英語

授課語言
2


輔導考照
1


輔導考照
2