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

-

Comments

Post a Comment

Popular posts from this blog

III-I CSE(AIML) ESSENTIALS OF MACHINE LEARNING

-
  ESSENTIALS OF MACHINE LEARNING 👉 Unit – I:  Introduction to Machine Learning What is machine learning, types of machine learning, applications of machine learning, preparing to model, modeling, and evaluation        👉 UNIT-1 (A) Introduction to Machine Learning Notes 👉 UNIT-1 (A) Introduction to Machine Learning PPTs 👉 UNIT-1 (B) Introduction to Machine Learning Notes 👉 UNIT-1 (B) Introduction to Machine Learning PPTs 👉 UNIT-1 Important Long Answer Question s 👉 UNIT 1 Important Long Answer Question YouTube Link 👉 Unit – II:  Supervised Learning: Regression Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, and Lasso Regression.         👉 UNIT-II Supervised Learning: Regression Notes  👉 UNIT-II Supervised Learning: Regression PPTs 👉 UNIT-2 Import...
Machine Learning

IT IV-I FUNDAMENTALS OF MACHINE LEARNING

-
  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,...
Machine Learning

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning