IT IV-I FUNDAMENTALS OF MACHINE LEARNING

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FUNDAMENTALS OF MACHINE LEARNING


Unit – I

Introduction andConcept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning. 

Concept learning task, Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias

👉UNIT-1 (A) Introduction Notes 

👉UNIT-1(B) Concept Learning PPTs

👉UNIT-1 Introduction and Concept Learning Notes

👉UNIT-1 Introduction and Concept Learning Long Answer Question

👉UNIT-1 Introduction & Concept Learning Important Questions YouTube Link


👉Fundamentals of Machine Learning YouTube Videos Links

Unit - II

Decision Treelearning and Neural Network learning: Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor, Issues in Decision Tree Learning. 

Introduction to Neural Network Learning, Introduction to Perceptions, Perceptions, Introduction to Gradient Descent and Back propagation.

👉UNIT-2 (A) Decision Tree Learning Notes 

👉UNIT-2 (A) Decision Tree Learning PPTs

👉UNIT-2(A) Decision Tree Learning Long Answer Questions

👉UNIT-2(A) Decision Tree Important Questions YouTube Link

👉UNIT-2 (B) Introduction to Neural Network Learning Notes

👉UNIT-2 (B) Introduction to Neural Network Learning PPT

👉UNIT-2 (B) Introduction to Neural Network Learning Long Answer Questions

👉UNIT-2 (B) Introduction to Neural Networks Long Answer Questions YouTube Link

👉Fundamentals of Machine Learning YouTube Videos Links

Unit - III

SupportVector Machines: Introduction, Linear Classifier, Non-linear Classifier, Training SVM, Support Vector Regression.

Bayesian Learning: Bayes theorem and concept learning, Minimum Description Length Principle, Bayes optimal classifier, Gibbs Algorithm, Naïve Bayes Classifier, The EM algorithm.

👉UNIT-3 (A) Support Vector Machine Notes

👉UNIT-3 (A) Support Vector Machine PPTs

👉SVM Program

👉Training SVM

👉Extra Info on SVM

👉UNIT-3(A) SVM Important Questions

👉UNIT-3 (A) SVM Short Answers and Long Answer Questions YouTube Link

👉Fundamentals of Machine Learning YouTube Videos Links

Unit – IV

Instance-basedTechniques: Introduction to Instance-based Techniques, Lazy vs. eager generalization, K nearest neighbor, Radial Basis Function, Case Based reasoning, Remarks on Lazy vs Eager learning.


👉Fundamentals of Machine Learning YouTube Videos Links

Unit – V

Genetic Algorithm: Biological motivation, Representing Hypothesis, Genetic Operators, Fitness function and selection, Hypothesis space search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

👉Fundamentals of Machine Learning YouTube Videos Links

👉YouTube Link1

👉YouTube Link 2


Text Books:

1.     Machine Learning - Mc Graw Hill, Tom M. Mitchell.

Reference Books:

1.     Machine Learning Methods and the Environmental Science, Neural Network, William W Hsieh Cambridge University Press.

2.     Rechard o Duda, Peter E. Hart and David G. Stork, & pattern Classification, John Wiley & Sons Inc,2001

3.     Chris Bishop, Neural Network for, Pattern Recognition, Oxford University Press. 1995

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