Machine Learning
- Introduction
- Concept Learning and the General to Specific Ordering
- Decision Tree Learning
- Artificial Neural Networks
- Bayesian Learning
- Instance-Based Learning
- Genetic Algorithms
- Learning Sets of Rules
- Analytical Learning
- Reinforcement Learning
UNIT-1 : Introduction & Concept Learning and the General to Specific Ordering
Introduction- Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning.
Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm.
👉Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs
👉Machine Learning 2- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs
👉Machine Learning 2 - UNIT-1 Questions
UNIT-2: Decision Tree Learning & Artificial Neural Networks
Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.
Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.
👉Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning
👉Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs
👉Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes
👉Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs
👉Machine Learning2 - UNIT 2 : Decision Tree and Artificial Neural Network Questions
UNIT-3: Bayesian Learning & Instance-Based Learning
Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.
Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.
👉Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes
👉Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs
👉Machine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs
👉Machine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs
👉Machine Learning2- UNIT-3 Questions
UNIT-4: Genetic Algorithms & Learning Sets of Rules
Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.
Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL
👉Machine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs
👉Machine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs
👉Machine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs
👉Machine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs
👉Machine Learning2: UNIT-4 Questions
UNIT-5: Analytical Learning & Reinforcement Learning
Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.
Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.
👉Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES
👉Machine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs
👉Machine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs
👉Machine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs
👉Machine Learning2: UNIT-5 Questions
Text Books:
1. Machine Learning – Tom M. Mitchell, – MGH
2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)
Reference Books:
1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ. Press.
2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.
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