CORE

Introduction to Machine Learning

24AIJ303 · S3 · 4 credits

Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.

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Modules
Module 01Overview of machine learning Introduction to Machine Learning (ML)expand

Types of Machine Learning- Supervised, Unsupervised, Reinforcement. Types of ML problems: Association, Classification and Regression. General Steps or Process of Machine Learning, Objective (Minimize Error or Cost Function), Cost functions: Definition and Types, Linear regression: with single and multiple variables (features), Least Squares method, Bias-Variance trade off.

Module 02Logistic Regression and Neural Networksexpand

Logistic Regression: Hypothesis representation, Decision boundary, Sigmoid Function and its differentiation. Neural Networks: Concept of perceptron and Artificial neuron, Activation Functions, Feed Forward Neural Network, Introduction to Gradient Descent and Back propagation Classification metrics: Cross validation and re-sampling methods, Classifier performance measures- Precision, recall, ROC curves.

Module 03Kernel machines and Instant Based Learningexpand

Kernel Machines- Support Vector Machine- Optimal Separating hyperplane, Kernel functions. Decision Trees- Entropy, Information Gain, ID3, Random Forest Ensemble methods: Boosting, Bagging, and Stacking

Module 04Probability and classificationexpand

Probability and classification: Naive Bayes and Gaussian class-conditional distribution, Bayes' Rule and Naive Bayes Model, Maximum Likelihood estimation. Discrete Markov Processes, Hidden Markov models.

Module 05Unsupervised learningexpand

Unsupervised Learning - Introduction to Clustering Methods-Categories, k-Means, Expectation-Maximization Algorithm. Dimensionality Reduction: PCA and LDA

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