Introduction to Machine Learning
Subject Overview
The second course in the AI/ML elective stream, covering the types of machine learning, supervised learning (regression, classification, perceptrons, SVM introduction) and unsupervised learning (K-Means, hierarchical clustering, DBSCAN), performance evaluation metrics, and the ethical considerations in applying ML to real-world problems. A 2-credit elective theory paper.
Unit-wise Syllabus
2 units — click WhatsApp below to get the full notes for each
Unit 1: Introduction to machine learning
Definition, history and applications of machine learning, types of ML (supervised, unsupervised, semi-supervised, reinforcement), labeled/unlabeled datasets, regression vs classification, training/validation/testing framework, performance metrics — confusion matrix, accuracy, precision, recall, F1 score, AUC
Unit 2: Supervised and unsupervised learning
Linear and non-linear regression, logistic regression, Naive Bayes, K-Nearest Neighbors, decision trees; introduction to artificial neural networks, perceptron learning algorithm, single-layer perceptron, introduction to SVM for linearly separable data; K-Means, hierarchical clustering, DBSCAN, clustering validation measures; ethical considerations and real-world applications of ML