Machine Learning Notes - Complete Course
This page contains complete notes for the Machine Learning course. All units are covered in detail, including theoretical concepts, examples, and key algorithms. You can directly read or download the PDFs embedded below.
Course Outline
Notes PDFs
Unit I: Introduction to Learning
Covers the basics of learning types — Supervised, Unsupervised, and Reinforcement Learning. It introduces Hypothesis Space, Inductive Bias, VC Dimension, PAC Learning, Model Selection, and Cross Validation methods for model evaluation.
📘 Unit 1 Notes
Unit II: Regression, Classification, and Feature Reduction
Explains Linear Regression, Decision Trees, K-Nearest Neighbour, and the process of Feature Selection and Dimensionality Reduction using PCA, LDA, and Factor Analysis.
📗 Unit 2 Notes
Unit III: Bayesian Learning and Clustering
Discusses Bayes Theorem, Naïve Bayes Classifier, and clustering algorithms such as K-Means and Hierarchical Clustering.
📙 Unit 3 Notes
Unit IV: Neural Networks and Logistic Regression
Introduces Linear Models, Logistic Regression, Perceptrons, and Multilayer Neural Networks with Backpropagation. Covers training methods and applications.
📕 Unit 4 Notes
Unit V: Kernel Methods and Ensemble Learning
Covers SVM, Kernel Tricks, Model Selection, and Ensemble techniques like Bagging and Boosting. It also explores real-world applications of Machine Learning.
📒 Unit 5 Notes
Suggested Readings:
Ethem Alpaydin, “Introduction to Machine Learning,” MIT Press, 2020.
Andreas Mulle, “Introduction to Machine Learning with Python,” O’Reilly, 2016.
Tom M. Mitchell, “Machine Learning,” McGraw Hill Education, 2017.
Chirag Shah, “A Hands-On Introduction to Machine Learning,” Cambridge University Press, 2022.