Advanced Machine Leaning
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Follow on Instagram for UpdatesAdvanced Machine Leaning Syllabus — Unit-wise Content
The official GTU curriculum for Advanced Machine Leaning (subject code ME02000321) as prescribed for CEE · Semester 2 divides the course into 6 units, covering 16 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 (ME02000321)Unit 1 Key concepts, Supervised/Unsupervised Learning, Loss functions
- and generalization
- Probability Theory
- Parametric vs Non- parametric methods
- Elements of Computational Learning Theory Ensemble Learning
- Random Forest
Unit 2 Kernel Methods for non-linear data, Support Vector Machines,
- Kernel Ridge Regression
- Structure Kernels
- Kernel PCA, Latent Semantic Analysis 08 15
Unit 3 Bayesian methods for using prior knowledge and data, Bayesian
- Bayesian Belief Networks and Graphical models
- Probabilistic Latent Semantic Analysis
- The Expectation- Maximisation (EM) algorithm
- Gaussian Processes Gaussian Processes
Unit 4 Dimensionality Reduction - CCA, LDA, ICA, NMF - Canonical
- Variates - Feature Selection vs Feature Extraction 08 25
Unit 5 Filter Methods - Sub-space approaches - Embedded methods
- Low-Rank approaches - Recommender Systems .Application areas - Security - Business - Scientific
Unit 6 Recent trends in supervised and unsupervised learning algorithm,
- dimensional reducibility
- feature selection and extraction 03 5
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