Unit 1 Introduction to Machine Learning

6 lecture hours

• Overview of Human Learning and Machine Learning
• Types of Machine Learning
• Applications of Machine Learning
• Tools and Technology for Machine Learning

Unit 2 Basic Machine Learning Algorithms

10 lecture hours

• Linear Regression
• Decision Trees
• Learning Decision Trees
• K-nearest Neighbour
• Collaborative Filtering
• Feature Selection
• Feature Extraction

Unit 3 Preparing to Model

10 lecture hours

• Machine Learning activities
• Types of data in Machine Learning
• Structures of data
• Data quality and remediation
• Data Pre-Processing: Dimensionality reduction
• Feature subset selection

Unit 4 Overview of Probability

• Statistical tools in Machine Learning
• Concepts of probability
• Random variables
• Discrete distributions
• Continuous distributions
• Multiple random variables
• Central limit theorem
• Sampling distributions
• Hypothesis space and inductive bias
• Evaluation and Cross Validation
• Hypothesis testing
• Monte Carlo Approximation. 10 w. e. f
• Academic Year: 2024-25 Semester: 1st Semester Category of the Course: PEC Program Name: Master of Engineering Level: PG Branch: Electronics & Communication Engineering Course / Subject Code: ME01004051 Course / Subject Name: Applied Machine Learning

Unit 5 Bayesian Concept of Learning

4 lecture hours

• Bayesian Learning
• Naïve Bayes
• Bayesian Network
• Exercise on Naïve Bayes

Unit 6 Logistic Regression and Support Vector Machine

6 lecture hours

• Logistic Regression
• Introduction to Support Vector Machine
• The Dual Formation
• Maximum Margin with Noise
• Nonlinear SVM and Kernel Function
• SVM: Solution to the Dual Problem

Unit 7 Introduction of Neural Network

10 lecture hours

• Early Models
• Perceptron Learning
• Training & Validation
• Parameter Estimation - MLE, MAP, Bayesian Estimation