## COURSE OBJECTIVES

• Learn how computers can learn from experience
• Learn and apply statistical techniques for classification and measuring their accuracy
• Apply supervised learning techniques for classification
• Apply un-supervised learning techniques for classification

### COURSE LEARNING OUTCOMES (CLO)

CLO: 1. Understand and describe how computers can learn from experience
CLO: 2. Use statistical techniques for classification and measuring their accuracy
CLO: 3. Apply supervised learning techniques for classification
CLO: 4. Apply un-supervised learning techniques for classification

### COURSE CONTENTS

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• Introduction, Problem Analysis, Adjustment of decision boundary.

• Introduction to Supervised and Unsupervised learning with examples; Why we Use Machine Learning techniques.

• Concept Learning by Induction, Hypothesis Representation

• Search in Hypotheses Space, Find-S Algorithm

• Version Space(Definition), List-then-Eliminate Algorithm

• Candidate Elimination Algorithm with example

• Bayes Theorem, Example

• Naïve Bayes Classifiers, Example, Learning to classify text

• Bayesian Belief Network, Example

• Model Evaluation and Selection (cross-validation, measuring error, Confusion Matrix, classification performance)

• Decision Tree Representation, Basic Algorithm,

• Information Gain measure, Example

• Gain Ratio, Gini Index Measures

• Inductive Bias in Decision Tree learning

• Dimensionality Reduction Intro, Subset selection,

• Principle Component Analysis, Example, Factor Analysis

• Support Vector Machine, Example,

• Optimal Separating Hyperplane

• Neuron, Perceptron, Basic Artificial neuron model, AND and OR operator non-linear problems,

• Activation functions, Delta learning rule

• Back propagation algorithm and training of hidden layer weights (1)

• Back propagation algorithm and training of hidden layer weights (2) with examples

• K-Nearest Neighbor Learning with Example,
• Locally Weighted Regression

• Linear Regression with Examples,

• Logistic Regression with Examples

• Unsupervised Learning- basics

• Partitioning Methods, K-Mean Clustering with Examples,

• K-Medoids Clustering with examples

• Hierarchical Clustering with examples

• AGNES Algorithm with Examples

• DIANA Algorithm with Examples
• Total Lectures