Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs

UNIT-1 (A)
Introduction 

 Introduction:
  • Well-Posed Learning Problems
  • Designing a Learning System
  • Perspectives and Issues in Machine Learning
  • Introduction to Supervised
  • Unsupervised and Reinforcement Learning

 

Machine Learning 2- UNIT-1 (A) Notes : Introduction & Concept Learning and the General to Specific Ordering Notes

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 Syllabus

Machine Learning 

UNIT - I

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.

 UNIT - II

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.

 UNIT - III

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.

 UNIT -IV

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    

UNIT - V

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.

 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.

About Machine Learning 2

Machine Learning 

πŸ‘‰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.

πŸ‘‰Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes

πŸ‘‰Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs

πŸ‘‰Machine Learning 2- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs

πŸ‘‰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.

πŸ‘‰Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning

πŸ‘‰Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs

πŸ‘‰Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes

πŸ‘‰Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs

πŸ‘‰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.

πŸ‘‰Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes

πŸ‘‰Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs

πŸ‘‰Machine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs

πŸ‘‰Machine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs

πŸ‘‰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

πŸ‘‰Machine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs

πŸ‘‰Machine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs

 πŸ‘‰Machine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs

πŸ‘‰Machine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs

πŸ‘‰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.

πŸ‘‰Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

πŸ‘‰Machine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs

πŸ‘‰Machine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs

πŸ‘‰Machine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs

πŸ‘‰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.





 



About Machine Learning

 

Machine Learning

πŸ‘‰  About Machine Learning 1

  • The Machine Learning Landscape
  • Classification
  • Support Vector Machines
  • Decision Trees
  • Ensemble Learning and Random Forests
  • Dimensionality Reduction
  • Clustering
  • πŸ‘‰ YouTube Link: https://www.youtube.com/@drrambabupemula

πŸ‘‰ About Machine Learning 2 
  • Introduction 
  • Data Pre-processing
  • Performance measurement of models 
  • Supervised Learning 
  • Decision Tree Learning 
  • Unsupervised Learning 
  • Ensemble Models

About Machine Learning 3

Machine Learning
  • Introduction 
  • Data Pre-processing
  • Performance measurement of models 
  • Supervised Learning 
  • Decision Tree Learning 
  • Unsupervised Learning 
  • Ensemble Models


Machine Learning Notes and PPTsπŸ‘‡

Unit1: Introduction and Data Preprocessing Notes
Unit1: Introduction and Data Preprocessing PPTs
UNIT 2 (A) Performance Measurement of Models Notes 
UNIT 2 (A) Performance Measurement of Models PPTs
UNIT 2(B): Supervised Learning Notes 
UNIT 3(A) Supervised Learning Notes
UNIT 3(A) Supervised Learning PPTs
UNIT 3(B) Decision Tree Learning PPTs
UNIT 4 : Unsupervised Learning PPTs
UNIT 5: Ensemble Models Notes













Machine Learning 3 - UNIT 5 Notes

About Machine Learning

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