برنامه درسی

آشنایی با دستگاه های ذخیره سازی و نحوه انتقال داده در معماری کامپیوتر

اصول طراح پایگاه داده اثر دیت

مقدمه

اصول ذخیره سازی

اصول طراحی داده

خصوصیات ACID

نرمال سازی

طراحی پایگاه داده

زبان جبری طراحی پایگاه داده

SQL

طراحی های پیشرفته پایگاه داده

آشنایی با نحوه مدیریت موثر داده در انواع حجم های پیشنهادی

آشنایی با آمار

Fudenberg, Drew, and Jean Tirole. Game Theory. MIT Press, 1991. ISBN: 9780262061414

1. Solution Concepts for Static Games

     a. Complete information: rationalizability, Nash equilibrium, epistemic foundations

     b. Incomplete information: Bayesian Nash equilibrium, interim correlated
         rationalizability

2. Solution Concepts for Extensive-form Games

     a. Backwards induction, subgame perfection, iterated conditional dominance

     b. Bargaining with complete information 

3. Equilibrium Concepts for Games with Imperfect Information

4. Signaling and Forward Induction

     a. Stable equilirium, the intuitive criterion, iterated weak dominance, epistemic
         foundations

5. Repeated Games

6. Reputation Formation 

     a. Reputation with short-lived opponents

     b. Screening and reputation in bargaining

7. Supermodular Games

8. Global Games

9. Cooperative Games

      a. Nash bargaining solution, core, Shapley value

      b. Non-cooperative implentations

آشنایی با ساختارهایی داده

کتاب اصول طراحی الگوریتم و ساختمان داده ها مشهور به CLRS

1. Introduction: Algorithm design paradigms - motivation; concepts of algorithmic efficiency; run-time analysis of algorithms; the Landau notation;
2. Divide-and-Conquer: Structure of divide-and-conquer algorithms; example applications from Binary Search, Integer Multiplication, Nearest Neighbour. Analysis of divide-and-conquer run-time recurrence relations.
3. Dynamic Programming: Form of dynamic programming algorithms; differences between dynamic programming and Divide-and-Conquer; example applications from: shortest path in graphs; ordering of matrix multiplications; longest common subsequence.
4. Greedy Methods: Overall view of greedy paradigm. Example of exact optimisation solution (Minimum spanning tree) and approximation solution (Integer Knapsack).
5. Graph Searching and traversal: Pervasiveness of graph models in applications and notion of search; combinatorial search (e.g. Knight's Tour); graph traversal methods: depth-first and breadth-first search;
6. Foundations of algorithms: Fundamental questions in the design of algorithms: does an algorithmic solution exist? does an efficient algorithmic solution exist? Models of algorithmic process and their universality: Church-Turing hypothesis.
7. Introduction to Computability: The existence of problems with no algorithmic solution; an example and proof that a specific computational problem has no algorithmic solution.
8. Computational Complexity: Quantification of resources used by algorithms: Time and Space; Complexity measures and Classes; Polynomial versus Non-Polynomial time complexity; the class P and motivation for viewing this as the set of tractable computational problems.
9. NP-completeness: Combinatorial search and optimisation problems; informal view of the case NP as problems with efficient checking algorithms; approaches to tackling the question of P = NP - informal review of NP-completeness, Cook's Theorem (without proof).

آشنایی با تکنیک های طراحی الگوریتم و نحوه استفاده آنها در الگوریتم های معمول و جدید