Advanced Machine Learning
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Advanced Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for Advanced Machine Learning (subject code 3175201) as prescribed for AI & ML · Semester 7 divides the course into 6 units, covering 14 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 (3175201)Unit 1 Machine Learning - Deep Learning - Difference between Machine Learning
- and Deep Learning - Machine Learning process - Probability Theory
- Parametric vs Non-parametric methods - Elements of Computational Learning Theory - Ensemble Learning: Bagging
- Random Forest
Unit 2 Neural Networks
- Biological motivation for Neural Network
- Neural network Representation-Applications -Perceptron-Backpropagation(first Phase and Second Phase) Multilayer Perceptrons: Introduction to Deep Learning-Applications- Multilayer Perceptron-Convolutional neural network-Recurrent neural network-Hybrid Neural network
Unit 3 Kernel Methods for non-linear data, Support Vector Machines, Kernel
- Ridge Regression
- Structure Kernels
- Kernel PCA, Latent Semantic Analysis 8 16%
Unit 4 Bayesian methods for using prior knowledge and data, Bayesian inference,
- Bayesian Belief Networks and Graphical models
- Probabilistic Latent 8 16% Subject Code : 3175201 Subject Name : Advanced Machine Learning Semantic Analysis
- The Expectation-Maximisation (EM) algorithm
- Gaussian Processes
Unit 5 Dimensionality Reduction - CCA, LDA, ICA, NMF - Canonical Variates -
- Feature Selection vs Feature Extraction 8 16%
Unit 6 Machine Learning Tools
- Tensor Flow
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