3- Intended Learning Outcome :-

a- Knowledge and Understanding :-

Students who complete the course will have the ability to :-

a1- describe mathematical formulation of computational and decisional problems.

a2- implement machine models, including non-deterministic machines, to simulate processes arising in industry and sciences.

a3- evaluate computation costs of algorithms and compare their efficiency on the basis of such algorithmic costs.

a4- explain at high level mathematical concepts and applications of the P = NP problem.

a5- describe computational complexity of randomized algorithms and their applications, e.g. in graph theory.

b. Intellectual Skills :-

Students who complete the course will have the ability to :-

b1- Analyze mathematical formulation.

b2- Analyze complexity of randomized algorithms and their applications.

b3- Analyze computational and decisional problems. 

b4- Deal with high level mathematical concepts and applications.

b5- Deal with Decidability and Complexity.

c-Professional Skills :-

Knowledge of the concepts and material presented in this course will provide the students with practical know-how to :- c1- Design Formalizing Algorithms.

c2- Design Multi-Tape Machines.

c3- Design memory advanced digital applications.

c4- Work effectively as an individual and as a member of a team. 

c5- Give technical presentations. 

c6- Work in stressful environment and within constraints.

c7-Use Turing Machines.

d-General Skills :-

Knowledge of the concepts and material presented in this course will provide the students with the capability to :-

d1- Be capable of applying both traditional and new concepts and skills.

d2- Work within and contribute to a team, apply management.