Pattern Recognition and Machine learning
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Pattern Recognition and Machine learning Syllabus — Unit-wise Content
The official GTU curriculum for Pattern Recognition and Machine learning (subject code 3730506) as prescribed for EACE · Semester 3 divides the course into 7 units, covering 57 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 (3730506)Unit 1 INTRODUCTION
- Pattern Recognition Systems
- The Design Cycle
- Learning and Adaptation
Unit 2 BAYESIAN DECISION THEORY
- Bayesian Decision Theory-Continuous Features
- Minimum-Error-Rate Classification
- Discriminant Functions, and Decision Surfaces
- Discriminant Functions for the Normal Density
- Error Probabilities and Integrals
- Error Bounds for Normal Densities
- Bayes Decision Theory- Discrete Features
- Missing and Noisy Features
- Bayesian Belief Networks
- Compound Bayesian Decision Theory and Context
Unit 3 MAXIMUM-LIKELIHOOD AND BAYESIAN
- PARAMETER ESTIMATION Maximum-Likelihood Estimation
- Bayesian Estimation
- Sufficient Statistics
- Problems of Dimensionality
- Component Analysis and Discriminants
- Expectation-Maximization (EM)
Unit 4 Subject Code
- AY 2018-19 4 LINEAR DISCRIMINANT FUNCTIONS Linear Discriminant Functions and Decision Surfaces
- Generalized Linear Discriminant Functions
- Minimizing the Perceptron Criterion Function
- Relaxation Procedures
- Nonseparable Behavior
- Minimum Squared-Error Procedures
- The Ho-Kashyap Procedures
- Linear Programming Algorithms
- Support Vector Machines
- Multicategory Generalizations
Unit 5 NEURAL NETWORKS
- Feedforward Operation and Classification
- Backpropagation Algorithm
- Error Surfaces
- Backpropagation as Feature Mapping
- Bayes Theory and Probability
- Practical Techniques for Improving Backpropagation
- Second-Order Methods
- Additional Networks and Training Methods
- Complexity Adjustment and Pruning
Unit 6 ALGORITHM-INDEPENDENT MACHINE LEARNING
- Lack of Inherent Superiority of Any Classifier
- Bias and Variance
- Resampling for Estimating Statistics
- Resampling for Classifier Design
- Estimating and Comparing Classifiers
- Combining Classifiers
Unit 7 UNSUPERVISED LEARNING AND CLUSTERING
- Mixture Densities and Identifiability
- Maximum-Likelihood Estimates
- Application to Normal Mixtures
- Unsupervised Bayesian Learning
- Data Description and Clustering
- Criterion Functions for Clustering
- Iterative Optimization
- Hierarchical Clustering
- The Problem of Validity
- On-line clustering
- Graph-Theoretic Methods
- Component Analysis
- Low-Dimensional Representations and Multidimensional Scaling (MDS)
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