Artificial Intelligence and Machine Learning (AL603 (C))-CSE (VI) | RGPV

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• Syllabus

• Syllabus - Artificial Intelligence and Machine Learning (AL603 (C))

• Introduction and mathematical Preliminaries

• Principles of pattern recognition
• Basics of linear algebra and vector spaces
• Eigen values, eigen vectors, Rank of matrix and SVD

• Pattern Recognition basics

• Bayesian decision theory, Classifiers, Discriminant functions, Decision surfaces
• Parameter estimation methods, Hidden Markov models
• Dimension reduction methods, Fisher discriminant analysis, Principal component analysis
• Non-parametric techniques for density estimation, nonmetric methods for pattern classification, unsupervised learning
• Algorithms for clustering: Kmeans, Hierarchical and other methods

• Feature Selection and extraction

• Problem statement and uses, Branch and bound algorithm, Sequential forward and backward selection
• Cauchy Schwartz inequality, Feature selection criteria function
• Feature Extraction: principles

• Visual Recognition

• Human visual recognition system
• Recognition methods
• Detection/Segmentation methods
• Context and scenes, Importance and saliency, Large-scale search and recognition
• Egocentric vision, systems, Human-in-the-loop interactive systems, 3D scene understanding

• Recent advancements in Pattern Recognition

• Comparison between performance of classifiers, Basics of statistics, covariance and their properties
• Data condensation, feature clustering, Data visualization
• Probability density estimation, Visualization and Aggregation, FCM and soft-computing techniques
• Examples of real-life datasets