Topics
|
Various
Texts |
Articles
/ Slides / Course /Book
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Other
(Web)
|
Introduction: What is ML?
|
[1]
[A:1], [DHS:1], [M:1], [HTF:1,2], [RN:18.1] |
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| Probability
101 |
[HTF:1,2]
[B: 2; AppendixB], [A:1],
[DHS:1], [M:1], [RN:18.1]
Book:
Grimstead/Snell:
"Introduction to Probability"
For info on Statistics, check out
Wackerly, D et al.,
Mathematical Statistics with Applications, 7th ed --
a good resource wrt probability theory, probability distributions, maximum likelihood estimation
On-line text:
Concepts and Applications of Inferential Statistics
|
Slides:
(from A. Moore)
STAT 221 (UofA: Kouritz)
PDF:
Cribsheet
|
Web: Conditonal Probably and Bayes' Rule
Relationships between
(Clickable) Inventory of Probability Distributions
Univariate Dist'n Rel'ns
Conjugate Priors
Conjugate #2
|
|
Learning Linear Regression Models |
[3,7]
[DHS: 3] [A:?]
|
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Applet |
| General Issues for Predictors
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[3.2, 3.4]
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Domingos, AAAI 2000
Domingos, ICML 2000
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Linear Classifiers
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[4]
[A: 11.2, 5.8, 10.1, 10.3, 105.-10.7; 4.1, 4.2, 4.5; 5.1,
5.2, 5.4-5.6], [HTF:5], [M:4], [RN:19-19.5]
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Slides:
Linear Classifiers (Moore)
DHS Ch5 Slides
Chapter (from T Mitchell)
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Applet |
Framework: Supervised Learning, Issues
Hypothesis
Testing for Evaluating Predictors
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[A: 14],[DHS:9], [M:5]
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DHS Ch9 Slides (ppt)
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Web: Simple Interactive Statistical Analysis
A New View of Statistics
Discussion of ROC curves
Statistical
Engineering website
|
Artificial Neural
Nets
(BackPropagation, Repr'n, Conjugate Gradient,
Topics)
|
[11]
[B: 5],
[A: 11], [M:4], [DHS:6], [RN:20.5]
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Slides:
DHS Ch6 Sildes
Article:
Intro to Conjugate Gradient w/out ... Pain
|
Web:
Neural Nets FAQ
|
Decision Trees
(Entropy, Pruning, Topics)
|
[9.2]
[B: 14.4]
[A: 9.1-9.2], [DHS:9], [M:3], [RN:18.3]
|
Book: C4.5:
Programs for Machine Learning
|
Web:
AIxploratorium
|
| Kernel Trick |
[6]
?Gaussian Process?
[A: ?]
[HTF ?4.5.2, 12.2-12.3], ...
|
Web:
Gaussian Process
|
Slides:
Dan Lizotte
Linear Algebra, etc: Matrix Cookbook
Cheat Sheet
|
Support Vector Machines
(Quadratic Programming, Kernel Trick, ...)
|
[HTF: 12]
[B: 6, 7, AppE],
[A: 10.9], [HTF 4.5.2, 12.2-12.3]
|
Slides:
SVM (R Meir)
Auxiliary notes
Books:
Vapnik:
"The nature of statistical learning theory"
[12 Xeroxed pages]
Kernel Methods for Pattern Analysis
Support Vector
Articles:
Intro to Kernel-Based Learning Algs
|
Web: Kernel Machines
Freeware:
SVM Light
Lagrange Multipliers:
SLIMY
GA
Tech Wikipedia
|
Computational Learning Theory
|
[7.1.5]
[A: 2.1-2.3], [M:7], [RN:18.5]
|
Book: An
Introduction to Computational Learning Theory
Ref: Probabilistic
inequalities
Factoid
WhoIsOccam
|
Course: Blum's
"Machine Learning Theory" |
Ensemble Methods
(Bagging, Boosting)
|
[B: 14.3],
[A: 15], [DHS: 9.5], [HTF:10]
|
Slides: Ensemble Learning
from
Ensemble
Methods in Machine Learning (Dietterich)
Notes from S Wang
|
Web:
Shapire, movie
AdaBoost Demo
Boosting.org
Boosting
Papers [UCSD]
Dietterich's Page
Papers on
Boosting/Bagging (Schapire)
Article:
"An Introduction to Boosting and Leveraging" (Meir/Raetsch),
On Bagging and Nonlinear Estimation (Friedman,Hall)
|
| Directed Models (Belief Nets)
|
[8]
[A: 3.7]
[RN:20.2]
[2.11], [3.10], [M:?], [RN:?] |
Slides:
Cmput366-Notes
on BeliefNets
GMM (Moore)
HMM (Moore)
Articles:
Heckerman:
A tutorial on learning with Bayesian networks
BN
- Classifiers
HMM (Rabiner)
|
Web: Belief Nets
Tutorials
Interactive Tutorial
Ten years of HMMs
|
Undirected Models
(Markov Random Fields)
|
[8.4]
|
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MaxEnt (Berger)
References (Wang)
|
Unsupervised
Learning
- K-means clustering
- Principle Components Analysis
- Independent Component Analysis
|
[12], [A: 7], [DHS:10], [HTF:14] |
|
Web: Tutorial
|
Reinforcement Learning
Markov Decision Process, Known Model,
Adaptive Dynamic Programming,
Value/Policy Iteration
TD-Learning
Value Approximation
Q Learning
|
[16], [M:13], [RN:20]
|
Book:
Reinforcement
Learning: An Introduction (download)
Slides: Barto
slides
ArticlesKaelbling's
survey article
Misc
pointers
POMDPs
for Dummies |
Web:Dietterich's
Reinforcement Learning
UMass Amherst
CMU
Reinforcement Learning Group |