Machine Learning
Subject Overview
The third Elective-II option, covering machine learning fundamentals and evaluation metrics, regression and classification (linear regression, KNN, logistic regression, Naive Bayes), ensemble methods and clustering (bagging, boosting, decision trees, random forests, k-means, SVM, PCA), and an introduction to time series and neural networks. A 4-credit elective theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: ML fundamentals and regression
What is machine learning, problems/data/tools, types of learning, performance evaluation measures (accuracy, precision, recall, F-measure), error metrics, data visualization, linear regression, gradient descent, closed-form/normal equations, features, overfitting, training/validation/test data
Unit 2: Classification
Classification problems and decision boundaries, K-Nearest Neighbor, logistic regression, probability and classification, Bayes optimal decisions, Naive Bayes and Gaussian class-conditional distribution
Unit 3: Ensemble methods and clustering
Bagging, decision trees and random forests, boosting, clustering (k-means, hierarchical agglomeration), Support Vector Machines, Principal Component Analysis
Unit 4: Time series and neural networks
Time series analysis, Markov models, autoregressive models, introduction to neural networks and deep learning