My Teaching Desk - By LokeshDev

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA B.Tech III Year II Semester – R23 Course: MACHINE LEARNING Time: 3 Hours Max Marks: 70 SECTION – I (50 Marks) Answer ALL questions (Each question carries 10 marks: a) 5 + b) 5) UNIT – I : Introduction to Machine Learning 1. a) Explain the Evolution of Machine Learning. Highlight major milestones and applications. (5M) b) Explain the Machine Learning system architecture and clearly describe the role of each stage. (5M) 2. a) Explain Data Sets in Machine Learning. Discuss training, validation, and test datasets. (5M) b) Explain Search and Learning in Machine Learning with a suitable example. (5M) UNIT – II : Nearest Neighbor–Based Models 3. a) Explain Distance Measures used in ML. Compare Euclidean, Manhattan, and Minkowski distances. (5M) b) Apply Radius Nearest Neighbor algorithm to classify a test instance. (5M) 4. a) Explain Proximity between binary patterns with suitable meas...

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA B.Tech III Year II Semester – R23 Course: MACHINE LEARNING Time: 3 Hours Max Marks: 70 SECTION – I (50 Marks) Answer ALL questions (Each question carries 10 marks: a) 5 + b) 5) UNIT – I : Introduction to Machine Learning 1. a) Define Machine Learning paradigms. Explain supervised and unsupervised learning with real-world examples. (5M) b) Explain Types of Data used in Machine Learning. How does data type affect algorithm selection? (5M) 2. a) Explain Data Representation in Machine Learning. Discuss different forms of representing data. (5M) b) Describe Model Selection and Model Learning. Explain their role in building an effective ML system. (5M) UNIT – II : Nearest Neighbor–Based Models 3. a) Explain Proximity Measures and Similarity Measures used in nearest neighbor models. (5M) b) Apply Manhattan and Euclidean distance measures to find the nearest neighbor for a given test instance. ...

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA B.Tech III Year II Semester – R23 MACHINE LEARNING Time: 3 Hours Max Marks: 70 SECTION – I (50 Marks) Answer ALL questions (Each question carries 10 marks: a) 5 + b) 5) UNIT – I (Introduction to Machine Learning) 1. a) Define Machine Learning. Explain its evolution with examples. (5M) b) Describe stages in Machine Learning process with neat diagram. (5M) 2. a) Differentiate Rote Learning, Induction, and Reinforcement Learning. (5M) b) Explain Data Acquisition and Feature Engineering. (5M) UNIT – II (Nearest Neighbor-Based Models) 3. a) Explain distance measures in KNN with formulas. (5M) b) Classify test point (3,4) using KNN (k=3): (1,2)-A, (2,3)-A, (5,6)-B, (6,7)-B (5M) 4. a) Explain non-metric similarity functions & binary proximity. (5M) b) Discuss KNN performance issues & improvements. (5M) UNIT – III (Decision Trees & Bayesian Models) 5. a) Explain ...