Dr. Rambabu Pemula

  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 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 N...

  Unit – III: Support Vector Machine- Bayesian Learning Support Vector Machines: 1.      Introduction 2.      Linear Classifier 3.      Non-linear Classifier 4.      Training SVM 5.      Support Vector Regression Bayesian Learning : 1.      Bayes theorem and concept learning 2.      Minimum Description Length Principle 3.      Bayes optimal classifier 4.      Gibbs Algorithm 5.      Naïve Bayes Classifier 6.      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 Long Answer Questions YouTube Link    

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 L...