Machine Learning
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Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Machine Learning (subject code 3735904) as prescribed for CS · Semester 3 divides the course into 6 units, covering 38 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3735904)Unit 1 Supervised Learning (Regression/Classification)
- Basic methods: Distance-based methods
- 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
Unit 2 Unsupervised Learning
- 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)
Unit 3 Evaluating Machine Learning algorithms and Model Selection,
- Introduction to Statistical Learning Theory
- Ensemble Methods (Boosting
- Random Forests)
Unit 4 Sparse Modelling and Estimation, Modelling Sequence/Time-Series
- Deep Learning and Feature Representation Learning
Unit 5 Scalable Machine Learning (Online and Distributed Learning) A
- selection from some other advanced topics
- Semi-supervised Learning
- Active Learning
- Reinforcement Learning
- Inference in Graphical Models
- Introduction to Bayesian Learning and Inference
Unit 6 Recent trends in various learning techniques of machine learning and
- classification methods for IOT applications
- Various models for IOT applications
- Subject Code: 3735904 w.e.f
- AY 2018-19 References: 1
- Machine Learning: A Probabilistic Perspective
- Kevin Murphy
- MIT Press
- The Elements of Statistical Learning
- Trevor Hastie
- Robert Tibshirani
- Jerome Friedman
- Springer 2009 (freely available online) 3
- Machine Learning in Action
- Peter Harrington
- dreamtech press 4
- Machine Learning for Big Data
- Jason Bell
- Machine Learning in Python
- Michael Bowles
- Machine Learning with TensorFlow for dummies
- Matthew Scarpino
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