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Machine Learning · PEC-IT701D

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Starts with 0.1 Introduction to Machine Learning: Definition, AI vs ML vs DLStart subject →

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Official MAKAUT Syllabus at a Glance

Unit Syllabus
1 Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes. Linear models: Linear Regression, Logistic Regression, Generalized Linear Models. Support Vector Machines, Nonlinearity and Kernel Methods. Beyond Binary Classification: Multi-class/Structured Outputs, Ranking
2 Clustering: K-means/Kernel K-means. Dimensionality Reduction: PCA and kernel PCA. Matrix Factorization and Matrix Completion. Generative Models (mixture models and latent factor models)
3 Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)
4 Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and Feature Representation Learning
5 Scalable Machine Learning (Online and Distributed Learning). A selection from some other advanced topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in Graphical Models, Introduction to Bayesian Learning and Inference
6 Recent trends in various learning techniques of machine learning and classification methods

Reading Guide

Topic Map

Unit 0 · Introduction to Machine Learning not in syllabus

These basics are asked in almost every paper, so they come first.

Unit 1 · Supervised Learning

Unit 2 · Unsupervised Learning

4 syllabus lines → 11 topics: each line is split into exam-sized topics (clustering → 2.1–2.3), feature selection and the curse of dimensionality get their own space, and 1 PYQ topic (recommender systems) comes from past papers.

Unit 3 · Model Evaluation and Selection

Unit 4 · Sparse Modeling, Sequences and Deep Learning

Unit 5 · Scalable and Advanced ML

Unit 6 · Recent Trends