Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

The course covers the following topics: Stochastic modeling techniques, statistical properties of stochastic processes, Poisson processes, random walks and Markov chains, entropy and relationship to probability theory and stochastic processes

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Basics of computer vision: Affine versus projective transformations, pinhole camera, camera calibration, stereo vision (depth from disparity). Review of Image representation (sampling &quantization), color, image versus video data. Low level image processing (point and region operations), Clustering and Image Segmentation, Types of features and their invariance properties, Linear Filters, Convolution, Correlation and Fourier Transform, Template Matching vs Features matching, Pattern Recognition Concepts (Features extraction, Features selection, Dimensionality Reduction (Principal Component Analysis), Bayes, NN, Decision Tree classifiers, Performance evaluation of a Classifier), Texture, shading, motion, tracking, optical flow. Skills in developing low, medium, high level vision tasks using MATLAB software package.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Basic definitions and constructs such as one-way functions, trapdoor permutations, symmetric and asymmetric cryptographic protocols, etc. Zero-knowledge proofs, secure distributed computation, chosen-ciphertext security, identity-based encryption, lattice-based cryptography, digital signatures, database privacy.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Mathematical Foundations, Linguistic Essentials, Corpus-Based Work. Collocations. Statistical Inference: n-gram models over sparse data. Word Sense Disambiguation. Lexical Acquisition. Markov Models. Part-Of-Speech Tagging. Probabilistic Context Free Grammars. Probabilistic Parsing. Statistical Alignment and Machine Translation. Clustering, Topics in Information Retrieval, Text Categorization.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

(Prerequisite: Introductory course in probability theory). Probabilistic reasoning in robotic systems, Probabilistic models of sensing and acting, Bayesian state estimation and filtering: Kalman filters and its variations (such as the extended Kalman filters) and particle filters, Modeling the robot's environment using Markov decision processes, Reinforcement learning paradigm for sequential decision making in unknown environments. Object recognition and modeling.