Dr. Rambabu Pemula

  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    

  FUNDAMENTALS OF MACHINE LEARNING (Professional Elective - 3) Department : IT IV B.Tech I Semester Course Code Hours/Week Credits Marks A2275E9 L T P C CIE SEE Total 3 0 0 3 40 60 100   Course Outcomes: At the end of the course, the student should be able to 1.      Ability to identify machine learning problems corresponding to different applications . 2.      Ability to understand the basic concepts such as Decision trees and Neural Networks. 3.      Apply Machine Learning algorithms to solve problems of moderate complexity. 4.      Identify Machine Learning applications 5.      Understand Genetic algorithms and their applications Unit – I Introduc...

  Bayes Theorem, Naive Bayes Algorithm, KNN Algorithm MCQs 1.      Formula for Bayes theorem is _______ Ans: A 2.      The bayesian network can be used to answer any query by using:- (A) Full distribution (B) Joint distribution (C) Partial distribution (D) All of the above Ans: B 3.        Which of the following is correct about the Naive Bayes? (A) Assumes that all the features in a dataset are independent (B) Assumes that all the features in a dataset are equally important (C) Both (D) None of the above Ans: C 4.        Naïve Bayes Algorithm is a ________ learning algorithm. (A) Supervised (B) Reinforcement (C) Unsupervised (D) None of these Ans: A   5.      Examples of Naïve Bayes Algorithm is/are (A) Spam filtration (B) Sentimental analysis (C) Classifying articles (D) All of the above Ans: D 6.        ...