- 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) PPTs: Introduction PPTs
Machine Learning 2- UNIT-1 (A) Notes : Introduction & Concept Learning and the General to Specific Ordering Notes
- Well-Posed Learning Problems
- Designing a Learning System
- Perspectives and Issues in Machine Learning
- Introduction to Supervised
- Unsupervised and Reinforcement Learning
- 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
- 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
πMachine 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.
πMachine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs
πMachine Learning 2- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs
πMachine 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.
πMachine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning
πMachine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs
πMachine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes
πMachine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs
πMachine 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.
πMachine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes
πMachine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs
πMachine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs
πMachine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs
πMachine 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
πMachine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs
πMachine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs
πMachine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs
πMachine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs
πMachine 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.
πMachine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES
πMachine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs
πMachine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs
πMachine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs
πMachine 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
- The Machine Learning Landscape
- Classification
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forests
- Dimensionality Reduction
- Clustering
- π YouTube Link: https://www.youtube.com/@drrambabupemula
- Introduction
- Data Pre-processing
- Performance measurement of models
- Supervised Learning
- Decision Tree Learning
- Unsupervised Learning
- Ensemble Models
About Machine Learning 3
- Introduction
- Data Pre-processing
- Performance measurement of models
- Supervised Learning
- Decision Tree Learning
- Unsupervised Learning
- Ensemble Models
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
About Machine Learning
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