466.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: 机器学习自学讨论
发信站: 南京大学小百合站 (Sun Dec 22 23:27:41 2002)

说老实话，我两年前为了找本机器学习的书，在上海跑了几个大书店，都没有，
感慨国内此领域力量之缺，现在有曾兄将大牛Mitchell的书给翻译过来，
我想该好好读读机器学习了，补补课，免得将来在这个行当混被人看出太无知，
决定在寒假放假之前把这本书看完。
水木上有zhuxd对此也很感兴趣，让我更决定自己好好学好，同时组织大家感兴趣的
也一起学习，讨论，可能我们还可以请周志华博士给咱们指导一下，看书的
前言，翻译的时候都指导过:)。
读书计划，是每天一节，节的定义根据Mitchell给他的学生安排的方式。见
http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-3/www/ml.html。
列在下面，这样每天可以自己看ps，（已经download下来）
ftp://211.80.38.17/DataMining/machinelearning/
然后读书，然后在板面写小结，然后讨论。
欢迎大家积极参与，共同提高。明天第一节。
1. Overview of learning (optional lecture). (Read Chapter 1 (not optional :-)) 
2. Concept learning, version spaces (ch. 2) 
3. Inductive bias, PAC learning (ch. 2, 7 up through 7.3) 
4. PAC learning, VC dimension, Mistake bounds (ch. 7.4 through 7.4.3, 7.5 thr
ough 7.5.3) (lecture slides same as Sept 17 lecture) 
5. Decision trees (ch. 3) 
6. Decision trees, overfitting, Occam's razor (ch. 3) 
7. Neural networks (ch. 4) 
8. Neural networks (ch. 4) 
9. Estimation and confidence intervals (ch. 5) Guest lecture: Prof. Larry Was
serman, Professor of Statistics, CMU 
10. Bayesian learning: MAP and ML learners (ch. 6) 
11. Bayesian learning: MDL, Bayes Optimal Classifier, Gibbs sampling (ch. 6) 
12. Naive Bayes and learning over text (ch. 6) 
13. Bayes nets (ch6) 
14. Midterm exam. open notes, open book. Results: midterm histograms for 15-6
81 and 15-781. 
15. EM and Combining labeled with unlabeled data (ch 6) 
16. Combining Learned Classifiers, Weighted Majority, Bagging (Ch 7: Weighted
 majority) 
17. Biological learning. Guest lecture: Prof. Jay McClelland, Director of Cen
ter for the Neural Basis of Cognition, CMU (see papers handed out) 
18. Boosting , Genetic algorithms, genetic programming (ch. 9) 
19. More on Genetic Programming (ch. 9) 
20. Instance based learning, k nearest nbr., locally weighted regression, Rad
ial basis functions (ch. 8, through 8.4). 
21. Support Vector Machines (see also Burges' SVM tutorial ) 
22. Learning Rules, Inductive Logic Programming (ch. 10) 
23. Reinforcement learning I (ch. 13) 
24. Reinforcement learning II (ch. 13) 
25. FINAL EXAM 

GzLi
http://[e-mail]
2003.12.22
--
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