Download MACHINE LEARNING Paper Code: ETCS

MACHINE LEARNING
Paper Code: ETCS-402 L T/P C
Paper: Machine Learning 3 0 3
Lecture
Unit I
1
Topic
Reference
B1, B5
3
Basic concepts: Definition of learning
systems, Goals and applications of machine
learning.
Aspects of developing a learning system:
training data, test data, concept
representation
Function approximation.
4
Types of Learning: Supervised learning ,
B1, B5
2
B1, B5
B1, B5
Vapnik-Chervonenkis (VC) Dimension,
Probably Approximately Correct (PAC)
Learning, Noise
5
6
7
8
Unit II
9
10
11
12
13
14
15
16
17
Unit III
18
19
20
21
22
23
Overview of classification: setup, training,
test, validation dataset, over fitting.
Classification Families: linear
discriminative, non-linear discriminative,
decision trees,
probabilistic (conditional and generative),
nearest neighbor.
B1, B5
Linear Regression and Logistic regression,
Perceptron, Exponential family,
Generative learning algorithms, Gaussian
discriminant analysis
Naive Bayes
Support vector machines: Optimal hyper
plane, Kernels.
Model selection and feature selection.
Combining classifiers: Bagging,
Boosting (The Ada boost algorithm)
Evaluating and debugging learning
algorithms, Classification errors.
B1, B5
B1, B5,B6
B1, B5
Unsupervised learning: Introduction to
Clustering. K-means.
EM Algorithm., Mixture of Gaussians.
B1, B5,B6
Factor analysis. PCA (Principal components
analysis)
ICA (Independent components analysis),
Latent semantic indexing.
Spectral clustering
B1, B5
B1, B5
B1, B5,B6
B1, B5
B1, B5,B6
B1, B5,B6
B1, B5
B1, B5,B6
B1, B5,B6
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B1, B5
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B1, B5,B6
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25
Unit IV
26
27
28
29
30
31
32
33
34
35
Markov models,
Hidden Markov models (HMMs).
B1, B5
B1, B5
Reinforcement Learning and Control:
Introduction and elements of reinforcement
learning.
MDPs.
Bellman equations
Value iteration and policy iteration,
Linear quadratic regulation (LQR).
LQG
Q-learning
Value function approximation,
Policy search. Reinforce
POMDPs.
B1, B5
B1
B1
B1, B5
L1
L2
B1
B1
B1
L3,L4
[B1] Tom M Mitchell, Machine Learning, McGraw Hill Education
[B2] Bishop, C. (2006). Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.
[B3] Duda, Richard, Peter Hart, and David Stork. Pattern Classification. 2nd ed. New York,
NY: Wiley-Interscience, 2000. ISBN: 9780471056690.
[B4] Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford
University Press, 1995. ISBN: 9780198538646.
[B5] Introduction to Machine Learning - Ethem Alpaydin, MIT Press, Prentice hall of India.
[B6] data Mining Concepts and Techniques. . Jiawei Han, Micheline Kamber, Jian Pei, 3rd
Edition
[L1 ]
https://www.researchgate.net/publication/2520656_Linear_Quadratic_Regulation_using_Rei
nforcement_Learning
[L2] https://www.youtube.com/watch?v=bpYKiqsNLZ4
[L3] https://www.youtube.com/watch?v=nJGa3Xxv0FM
[L4] http://cs.brown.edu/research/ai/pomdp/tutorial/pomdp-background.html