Dr. Rambabu Pemula

Unit – V Ensemble Learning and Reinforcement Learning 1.      Ensemble Methods: a.       Bagging b.      Boosting  c.       Random Forests 2.      Reinforcement Learning: a.       Overview b.      Example c.       Getting Lost d.      Markov Decision Process e.       Values f.       difference between SARSA and Q Learning g.      Uses of Reinforcement Learning  

  ESSENTIALS OF MACHINE LEARNING Course Outcomes: At the end of this course, students will be able to: 1. Understand the Concepts of Machine Learning. 2. Develop Simple Regression Models. 3. Build various classification algorithms. 4. Analyse the need of ensemble learning and dimension reduction. 5. Apply the Clustering algorithms for developing applications. Unit – I:  Introduction to Machine Learning What is Machine Learning, Types of Machine Learning, Applications of Machine learning, preparing to Model, Modelling and Evaluation 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, Lasso Regression. Unit – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, L...