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  • 8:02 PM, Saturday, 16 Oct 2021


Course Postgraduate
Semester Sem. I
Subject Code AVD614
Subject Title Pattern recognition and Machine Learning for data processing

Syllabus

Human visual system and image perception; monochrome and colour vision models; image digitization, display and storage; 2 ‐ D signals and systems; image transforms ‐ 2D DFT, DCT, KLT, Harr transform and discrete wavelet transform; image enhancement: histogram processing, spatial ‐ filtering, frequency ‐ domain filtering; image restoration: linear degradation model,inverse filtering, Wiener filtering; image compression: lossy and lossless compression, image compression standards, image analysis: edge and line detection, segmentation, feature extraction, classification; image texture analysis; morphological image processing: binary morphology ‐ erosion, dilation, opening and closing operations, applications; basic gray ‐ scale morphology operations; colour image processing: colour models and colour image processing Fundamentals of digital video processing ‐ Coverage includes spatio ‐ temporal sampling, motion analysis, parametric motion models, motion ‐ compensated filtering, and videoReview: Linear Algebra, Matrix Calculus, Probability and Statistics. Supervised Learning: Linear Regression (Gradient Descent, Normal Equations), Weighted Linear Regression (LWR), Logistic Regression, Perceptron, Newton's Method, KL-divergence, (cross-)Entropy, Natural Gradient, Exponential Family and Generalized Linear Models, Generative Models (Gaussian Discriminant Analysis, Naive Bayes), Kernel Method (SVM, Gaussian Processes), Tree Ensembles (Decision trees, Random Forests, Boosting and Gradient Boosting), Learning Theory, Regularization, Bias-Variance Decomposition and Tradeoff, Concentration Inequalities, Generalization and Uniform Convergence, VC-dimension, Deep Learning: Neural Networks, Backpropagation, Deep Architectures, Unsupervised Learning, K-means, Gaussian Mixture Model (GMM), Expectation Maximization (EM), Variational Auto-encoder (VAE), processing operations.

Factor Analysis, Principal Components Analysis (PCA), Independent Components Analysis (ICA), Reinforcement Learning (RL) : Markov Decision Processes (MDP), Bellmans Equations, Value Iteration and Policy Iteration, Value Function Approximation, Q-Learning, Application: Advice on structuring an ML project, Evaluation Metrics, Missing data techniques and tracking, Special Topic: Computer Vision. Special Topic: NLP, Special topic: Machine listening and Music Information Retrieval, Special Topic: Speech, Special Topic: Compressive Sensing, Special topics: Array processing, beamforming, independent component analysis, MIMO/SIMO models, under-constrained separation, spectral factorizations

Text Books

1. Pattern Recognition Machine Learning by Bishop

References