• Lesson 1: What is Game Theory?
• Lesson 2: Importance and Applications of Game Theory
• Lesson 3: Types of Games (Zero-Sum, Non-Zero-Sum, etc.)
• Lesson 4: Basic Assumptions in Game Theory
• Lesson 5: Overview of the Course

• Lesson 1: Nash Equilibrium
• Lesson 2: Pareto Optimality
• Lesson 3: Minimax Theorem
• Lesson 4: Dominant and Dominated Strategies
• Lesson 5: Rationality and Strategic Thinking

• Lesson 1: Normal Form Representation
• Lesson 2: Pure Strategy Nash Equilibrium
• Lesson 3: Mixed Strategy Nash Equilibrium
• Lesson 4: Payoff Matrices and Utility Theory
• Lesson 5: Applications of Static Games

• Lesson 1: Extensive Form Representation
• Lesson 2: Subgame Perfect Equilibrium
• Lesson 3: Backward Induction
• Lesson 4: Sequential Equilibrium
• Lesson 5: Applications of Dynamic Games

• Lesson 1: Cooperative vs Non-Cooperative Games
• Lesson 2: Coalition Formation
• Lesson 3: The Core of a Game
• Lesson 4: Shapley Value
• Lesson 5: Applications of Cooperative Games

• Lesson 1: Introduction to Repeated Games
• Lesson 2: Discounted Payoffs
• Lesson 3: Finitely vs Infinitely Repeated Games
• Lesson 4: Stochastic Games Basics
• Lesson 5: Applications in Real-World Scenarios

• Lesson 1: Introduction to Auctions
• Lesson 2: Types of Auctions (English, Dutch, Vickrey, etc.)
• Lesson 3: Revenue Equivalence Theorem
• Lesson 4: Basic Mechanism Design Principles
• Lesson 5: Applications of Auctions

• Lesson 1: Evolutionarily Stable Strategy (ESS)
• Lesson 2: Fitness Landscapes
• Lesson 3: Replicator Dynamics
• Lesson 4: Application of Evolutionary Concepts
• Lesson 5: Limitations of Evolutionary Models

• Lesson 1: Modeling Strategic Interactions on Networks
• Lesson 2: Network Formation Games
• Lesson 3: Congestion Games
• Lesson 4: Potential Games
• Lesson 5: Applications in Communication and Transport Networks

• Lesson 1: Fictitious Play
• Lesson 2: Reinforcement Learning in Games
• Lesson 3: Epsilon-Equilibrium
• Lesson 4: Exploration vs Exploitation in Learning
• Lesson 5: Applications of Learning in Strategic Settings

• Lesson 1: Introduction to Bargaining Theory
• Lesson 2: Nash Bargaining Solution
• Lesson 3: Fair Division Principles
• Lesson 4: Cooperative Bargaining Games
• Lesson 5: Applications in Negotiation

• Lesson 1: Introduction to Signaling Games
• Lesson 2: Perfect Bayesian Equilibrium
• Lesson 3: Inspection Game Basics
• Lesson 4: Principal-Agent Problems
• Lesson 5: Applications in Economics

• Lesson 1: Understanding Mixed Strategies
• Lesson 2: Computing Mixed Strategy Equilibria
• Lesson 3: Correlated Equilibria Basics
• Lesson 4: Examples of Correlation in Games
• Lesson 5: Applications in Coordination Problems

• Lesson 1: Quantitative Game Theory Basics
• Lesson 2: Incorporating Human Behavior in Games
• Lesson 3: Quantal Response Equilibrium
• Lesson 4: Limitations of Behavioral Models
• Lesson 5: Applications in Psychology

• Lesson 1: Advanced Payoff Analysis
• Lesson 2: Dual Game Theory Concepts
• Lesson 3: Network Effects in Static Games
• Lesson 4: Advanced Sequential Equilibria
• Lesson 5: Applications in Strategic Decision-Making

• Lesson 1: Introduction to Computational Complexity in Games
• Lesson 2: Algorithms for Nash Equilibria
• Lesson 3: Approximation Algorithms
• Lesson 4: Complexity Classes (PPAD, etc.)
• Lesson 5: Applications in Computer Science

• Lesson 1: Principles of Mechanism Design
• Lesson 2: Incentive Compatibility
• Lesson 3: Revenue Optimization in Auctions
• Lesson 4: Vickrey-Clarke-Groves (VCG) Mechanisms
• Lesson 5: Cryptoeconomic Mechanism Design

• Lesson 1: Advanced Replicator Dynamics
• Lesson 2: Evolutionary Stability in Large Populations
• Lesson 3: Evolution of Cooperation
• Lesson 4: Game Dynamics in Ecology
• Lesson 5: Applications in Biology and Economics

• Lesson 1: Advanced Stochastic Game Models
• Lesson 2: Bellman Equations in Dynamic Games
• Lesson 3: Strategy Optimization under Uncertainty
• Lesson 4: Markov Decision Processes
• Lesson 5: Applications in AI and Robotics

• Lesson 1: Introduction to Mean Field Theory
• Lesson 2: Approximation of Large Population Games
• Lesson 3: Equilibrium Computation in Mean Field Games
• Lesson 4: Applications in Economics and Social Sciences
• Lesson 5: Limitations and Challenges

• Lesson 1: Advanced Coalition Formation Models
• Lesson 2: Value Allocation Methods
• Lesson 3: Stability of Coalitions
• Lesson 4: Applications in Collaborative Systems
• Lesson 5: Case Studies

• Lesson 1: Modeling Adversarial Interactions
• Lesson 2: Security Games
• Lesson 3: Applications in AI and Cybersecurity
• Lesson 4: Game-Theoretic Planning in Robotics
• Lesson 5: Challenges and Future Directions

• Lesson 1: Advanced Bayesian Nash Equilibrium
• Lesson 2: Information Asymmetry in Games
• Lesson 3: Designing Bayesian Mechanisms
• Lesson 4: Applications in Auctions and Negotiation
• Lesson 5: Limitations of Bayesian Models

• Lesson 1: Regret Minimization Algorithms
• Lesson 2: Multi-Agent Reinforcement Learning
• Lesson 3: Online Learning in Games
• Lesson 4: Adaptive Strategies in Repeated Games
• Lesson 5: Applications in Autonomous Systems

• Lesson 1: Psychological and Social Insights in Games
• Lesson 2: Experimental Game Theory
• Lesson 3: Fairness and Reciprocity
• Lesson 4: Bounded Rationality
• Lesson 5: Applications in Behavioral Economics

• Lesson 1: Overview of Nashpy
• Lesson 2: Installation and Setup
• Lesson 3: Representing Games in Nashpy
• Lesson 4: Computing Nash Equilibria
• Lesson 5: Visualization in Nashpy
• Lesson 6: Mixed Strategy Equilibria
• Lesson 7: Iterative Methods for Equilibrium Calculation
• Lesson 8: Solving Real-World Problems with Nashpy
• Lesson 9: Performance Optimization and Scaling
• Lesson 10: Extending Nashpy and Future Directions

• Lesson 1: Overview of Gambit
• Lesson 2: Installation and Setup
• Lesson 3: Representing Games in Gambit
• Lesson 4: Computing Nash Equilibria in Gambit
• Lesson 5: GUI-Based Usage of Gambit
• Lesson 6: Mixed Strategy and Correlated Equilibria
• Lesson 7: Extensive-Form Games and Subgame Perfect Equilibria
• Lesson 8: Solving Real-World Problems with Gambit
• Lesson 9: API Integration and Scripting
• Lesson 10: Recent Research and Extensions

Course Duration: about 100+20 hours

The online class is held via Skype (or Zoom or Microsoft Teams) and the cost per hour of tutoring is only $15. At the end of this long course, you will master all the required basic and advanced concepts of Game Theory and we will accomplish a real-world project together for about 20 hours with Python Libraries, which fully prepares you to find a job as a Game Theory Expert in AI job markets.
To book this class, message or call my telegram or WhatsApp:
+98 (912) 490-8372 or +98 (935) 490-8372.
You can also send email to me:
abolfazl.mohammadijoo@gmail.com