About Machine Learning 2

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

👉Machine Learning 2 Syllabus

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) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes

👉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.