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...

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...

  ML: Intro to Machine Learning Q1.  Applications of the Supervised Learning Select the problem statements where you can apply supervised algorithms. 1.      For an e-commerce website, segmenting the unlabelled customers based on their behaviour from a large dataset. 2.      Given data on crop yields over the last 50 years, trying to predict next year's crop yields. 3.      Based on data samples of webpages, classifying a webpage whether the content on the web page should be considered "child friendly" or "adult". 4.      Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different groups of such patients.   Ans: Correct Answer: Given data on crop yields over the last 50 years, trying to predict next year’s crop yields. Based on data samples of webpages, classifying a webpage whether the content...