makautnotes

Machine Learning · Reading Time

Time to read each topic once: its main content only (the PYQs and ⚡ Quick recall boxes are extra), for an average reader.

By Unit

Unit Written Time
Unit 0 · Introduction to Machine Learning 4/4 20 min
Unit 1 · Supervised Learning 13/13 54 min
Unit 2 · Unsupervised Learning 11/11 50 min
Unit 3 · Model Evaluation and Selection 9/9 39 min
Unit 4 · Sparse Modeling, Sequences and Deep Learning 6/6 30 min
Unit 5 · Scalable and Advanced ML 5/5 23 min
Unit 6 · Recent Trends 2/2 8 min
Total 50/50 3 h 44 min

Unit 0 · Introduction to Machine Learning

Topic Time
0.1 Introduction to Machine Learning: Definition, AI vs ML vs DL ★★ 8 min
0.2 Types of Machine Learning ★★★ 4 min
0.3 Data Basics: Features, Variable Types, Data Splits & Pre-processing ★ 5 min
0.4 Parametric vs Non-parametric Models ★★★ 3 min

Unit 1 · Supervised Learning

Topic Time
1.1 Supervised Learning: Classification vs Regression ★★ 3 min
1.2 Distance-Based Methods & Distance Measures ★★ 4 min
1.3 K-Nearest Neighbours (KNN) ★★★ 5 min
1.4 Decision Trees: ID3, Entropy & Information Gain ★★ 6 min
1.5 Probability Basics & Bayes Theorem ★ 4 min
1.6 Bayesian Learning & Inference ☆ 4 min
1.7 Naive Bayes Classifier ★★ 5 min
1.8 Linear Regression ★★★ 6 min
1.9 Logistic Regression ★★★ 4 min
1.10 Generalized Linear Models ☆ 2 min
1.11 Support Vector Machines: Hyperplane, Margin & Support Vectors ★ 4 min
1.12 Non-linearity & Kernel Methods (Non-linear SVM) ★ 4 min
1.13 Beyond Binary Classification: Multi-class, Structured Outputs & Ranking ☆ 3 min

Unit 2 · Unsupervised Learning

Topic Time
2.1 Clustering: Concepts, Requirements & Types ★ 6 min
2.2 K-means Clustering ★★★ 6 min
2.3 Kernel K-means ☆ 3 min
2.4 Dimensionality Reduction & the Curse of Dimensionality ★★★ 4 min
2.5 Feature Selection ★★ 4 min
2.6 Principal Component Analysis (PCA) ★★★ 5 min
2.7 Kernel PCA ★ 3 min
2.8 Recommender Systems: Content-Based & Collaborative Filtering ★★ 5 min
2.9 Matrix Factorization & Matrix Completion ★★ 6 min
2.10 Generative Models: Mixture Models (GMM & EM) ★★ 5 min
2.11 Latent Factor Models ☆ 3 min

Unit 3 · Model Evaluation and Selection

Topic Time
3.1 Confusion Matrix & Classification Metrics ★★★ 5 min
3.2 ROC, AUC & Other Evaluation Metrics ☆ 4 min
3.3 Overfitting, Underfitting & the Bias–Variance Trade-off ★★★ 5 min
3.4 Model Selection & Cross-Validation ★★ 5 min
3.5 Introduction to Statistical Learning Theory (PAC, VC Dimension) ★ 4 min
3.6 Ensemble Learning: Principle & Types ★★★ 4 min
3.7 Bagging & Bootstrapping ★★★ 4 min
3.8 Boosting & Bagging vs Boosting ★★★ 4 min
3.9 Random Forest ★★ 4 min

Unit 4 · Sparse Modeling, Sequences and Deep Learning

Topic Time
4.1 Sparse Modeling & Estimation ★★ 6 min
4.2 Modeling Sequence / Time-Series Data ★ 4 min
4.3 Neural Network Fundamentals: Perceptron, Activation Functions & Backpropagation ★★★ 6 min
4.4 Deep Learning: Concepts, ML vs DL & Applications ★★★ 5 min
4.5 Deep Learning Architectures: CNN, RNN, Autoencoders, DBN ☆ 5 min
4.6 Feature Representation Learning ★ 4 min

Unit 5 · Scalable and Advanced ML

Topic Time
5.1 Scalable ML: Online & Distributed Learning ★ 5 min
5.2 Semi-supervised Learning ★ 3 min
5.3 Active Learning ★★ 4 min
5.4 Reinforcement Learning ★★★ 6 min
5.5 Inference in Graphical Models: Bayesian Networks ★ 5 min
Topic Time
6.1 Recent Trends in Learning Techniques ☆ 5 min
6.2 Recent Trends in Classification Methods ☆ 3 min