Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Implementation of database systems on modern hardware systems. Considerations concerning operating system design including: buffering, page size, pre-fetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems for large-scale database systems. Implementation of distributed databases.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Introduction: Computational Intelligence, Hard versus Soft Computing, Data driven versus Rule driven Modeling approaches. Neural Networks, artificial versus biological neurons. Networks, layers, transfer functions, perceptron, the learning process, back propagation algorithm for training feedforward NNs, recurrent networks, Radial Basis Function (RBF) Networks Support Vector Machines, applications to functions approximation, classification, vision and control problems. Classical sets and fuzzy sets, membership functions, classical relations and fuzzy relations, fuzzy–to-crisp conversions. Fuzzy rules, Fuzzy Inference System (FIS), Mamdani and Sugeno fuzzy inference systems, MATLAB FIS building tools, Neuro-Fuzzy Systems (ANFIS), Soft RBF or Fuzzy Additive Models (FAMs), FIS vs. FAM, Fuzzy clustering/ classification, Fuzzy pattern recognition, Fuzzy control systems.

Faculty : Faculty of Engineering and Applied Sciences

School :

Course Description :

This course gives an overview of many models and algorithms used in modern machine learning, including linear and non-linear models, multi-layer neural networks, support vector machines, deep belief networks, density estimation methods, Bayesian belief networks, graphical models, mixture models, clustering, ensemble methods, and reinforcement learning.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Distributed Problem Solving. Distributed Planning. Game Theory: Competition and Coordination. Multi-Agent Learning. Protocols for Agents. Agents development frameworks and languages. Agent oriented methodologies. Agent oriented analysis and design.

Faculty : Faculty of Engineering and Applied Sciences

School : Computer Science and Engineering

Course Description :

Deductive verification methods (theorem proving), Linear Time Logic/ Computational tree logic, Binary decision diagrams and symbolic model checking. State explosion problem, partial order reduction, abstraction. Verification tools. High Order Logic. Hardware and software specification and verification.