Dr. Rambabu Pemula

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-Nearest Neighbour Algorithm 4.     Logistic Regression 5.     Decision Trees: a.      Construction b.     classification and regression trees c.      example 6.     Naive Bayesian Classifier 7.     Support Vector Machines: a.      Optimal Separation b.     Kernels c.      Algorithm 👉 UNIT-II (A) Supervised Learning Classification Notes 👉 UNIT-III (A) Supervised Learning Classification PPTs 👉 UNIT-III(A) Important Long Answer Questions 👉 UNIT-III (A) Supervised Learning: Classification Important Questions YouTube Link

  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...

 Machine Learning - Support Vector Machines (SVM) - MCQs 1. A Support Vector Machine can be used for A.     Performing linear or nonlinear classification B.     Performing regression C.     For outlier detection D.     All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A.     Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called  Soft margin classification  Hard margin classification  Strict margin classification  Loose margin classification Ans: B 5. The main issues with hard margin classification are It only works if the data is linearly sep...