Machine Learning
Unit I: Introduction: Towards Intelligent Machines Well posed Problems, Example of Applications in diverse
fields, Data Representation, Domain Knowledge for Productive use of Machine Learning, Diversity of Data:
Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining, Basic Linear Algebra in
Machine Learning Techniques.
Unit II: Supervised Learning: Rationale and Basics: Learning from Observations, Bias and Why Learning
Works: Computational Learning Theory, Occam's Razor Principle and Over fitting Avoidance Heuristic Search
in inductive Learning, Estimating Generalization Errors, Metrics for assessing regression, Metris for assessing
classification.
Unit III: Statistical Learning: Machine Learning and Inferential Statistical Analysis, Descriptive Statistics in
learning techniques, Bayesian Reasoning: A probabilistic approach to inference, K-Nearest
Neighbor Classifier. Discriminant functions and regression functions, Linear Regression with Least Square
Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and Thresholding for
Classification, Minimum Description Length Principle.
Unit IV: Support Vector Machines (SVM): Introduction, Linear Discriminant Functions for Binary
Classification, Perceptron Algorithm, Large Margin Classifier for linearly seperable data, Linear Soft Margin
Classifier for Overlapping Classes, Kernel Induced Feature Spaces, Nonlinear Classifier, and Regression by
Support vector Machines.
Learning with Neural Networks: Towards Cognitive Machine, Neuron Models, Network Architectures,
Perceptrons, Linear neuron and the Widrow-Hoff Learning Rule, The error correction delta rule.
Unit V: Multilayer Perceptron Networks and error back propagation algorithm, Radial Basis Functions
Networks. Decision Tree Learning: Introduction, Example of classification decision tree, measures of impurity
for evaluating splits in decision trees, ID3, C4.5, and CART decision trees, pruning the tree, strengths and
weakness of decision tree approach.
https://www.scribd.com/document/765822959/machine-learning-1
1. a) Compare Structured and Unstructured, Forms of Learning.
b) Explain about Productive use of Machine Learning
OR
2. a) Analyze various Applications in diverse fields
b) Explain about Data Representation
UNIT-II
3. Compare Metrics for assessing regression and classification.
OR
4. Discuss about Computational Learning Theory and Occam's Razor Principle.
UNIT-III
5. a) Discuss about K-Nearest Neighbor Classifier.
b) Compare Linear Regression and Logistic Regression.
OR
6. a) Explain about Fisher's Linear Discriminant.
b) Explain about Minimum Description Length Principle.
UNIT-IV
7. a) Discuss about Regression by Support vector Machines
b) Explain about Widrow-Hoff Learning Rule in Neural Networks.
OR
8. a) Explain about Neuron Models in Neural Networks.
b) Discuss about Perceptron Algorithm in SVM.
UNIT-V
9. Explain about Decision Tree Learning of Machine Learning.
OR
10. a) Explain about error back propagation algorithm.
b) Discuss about Multilayer Perceptron Networks.
