Logic and methods of proofs. Basic Discrete Structures: Sets, Functions, Recursive definitions, Sequences and Summations. Growth of functions. Integers and Division, Rings & Fields, Applications of Number Theory. Combinatorics: Counting techniques, Permutations and Combinations, Binomial Coefficients, Permutation and Combinations with repetition, Recurrence Relations, Generating Functions. Discrete Structures: Relations, Graphs, Trees and Finite State Machines. Discrete Probability (optional).

Abstract Data Types; Performance Measurement: Time & Space Complexity, Big-O notation. Basic Data Structures: Lists, Stacks, Queues, Priority Queues. Trees: Recursion, Terminology, General Trees, Binary Trees, Balanced Search Trees, AVL Trees. Multi-way Trees: the family of B-Trees. Heaps: Min and Max Heap, HeapSort, Priority Queue as a Heap. Hashing Techniques: Hashing Functions, Collision Resolution Strategies. Graphs: Terminology, Graph Traversal Algorithms, Other important graph algorithms, Sorting.

Introduction to basic computer organization and how the computer works; The Internal organization of the Intel x86-based PCs; Basic Computer Organization [sequential machines, DeMorgan’s Law, simplifying circuits using Karnaugh maps, Instruction code, Computer registers, Instruction set, Timing and control]; Register Transfer Language HDL ( Hardware Description Language) and Micro-operations; Computer Arithmetic Logic Unit Design.

Approaches to machine learning: Explanation-based learning - Learning by observation and discovery - Analogical and Case-based Learning - Learning Models - Evaluation of Learning Algorithms - Experimental Methodology - Empirical Learning - Reinforced Learning and Genetic algorithms
Neural Computations: examples and applications History of Artificial Neural System development - Fundamental Concepts and Models of Artificial Neural systems. Applications: Neural Network Simulation and Implementations and other emerging applications of Neural Algorithms and Systems.