• Lesson 1: What is Artificial Intelligence?
• Lesson 2: History and Evolution of AI
• Lesson 3: AI vs Machine Learning vs Deep Learning
• Lesson 4: Applications of AI in the Real World
• Lesson 5: Overview of the Course

• Lesson 1: Key Concepts in AI
• Lesson 2: Symbolic AI vs Statistical AI
• Lesson 3: Agents and Environments
• Lesson 4: The Turing Test and AI Benchmarks
• Lesson 5: Ethical and Social Implications of AI

• Lesson 1: Problem-Solving Agents
• Lesson 2: State-Space Representation
• Lesson 3: Search Strategies (DFS, BFS)
• Lesson 4: Heuristic Search (A*, Greedy Search)
• Lesson 5: Constraint Satisfaction Problems (CSPs)

• Lesson 1: Introduction to Logic in AI
• Lesson 2: Propositional Logic
• Lesson 3: First-Order Logic
• Lesson 4: Logical Inference Techniques
• Lesson 5: Applications of Logical Reasoning

• Lesson 1: What is Knowledge Representation?
• Lesson 2: Semantic Networks
• Lesson 3: Ontologies and Taxonomies
• Lesson 4: Frames and Scripts
• Lesson 5: Knowledge-Based Systems

• Lesson 1: What are Expert Systems?
• Lesson 2: Architecture of Expert Systems
• Lesson 3: Rule-Based Systems
• Lesson 4: Inference Engines
• Lesson 5: Examples of Expert Systems

• Lesson 1: Introduction to Planning in AI
• Lesson 2: Planning Algorithms
• Lesson 3: STRIPS Representation
• Lesson 4: Partial-Order Planning
• Lesson 5: Applications of Planning

• Lesson 1: Basics of Robotics
• Lesson 2: AI in Robot Perception
• Lesson 3: Motion Planning
• Lesson 4: Human-Robot Interaction
• Lesson 5: Applications of AI in Robotics

• Lesson 1: Evolutionary Computation Overview
• Lesson 2: Simulated Annealing
• Lesson 3: Swarm Intelligence (Ant Colony, PSO)
• Lesson 4: Applications of Natural Computation
• Lesson 5: Ethical Considerations

• Lesson 1: AI Development Tools and Languages
• Lesson 2: Basics of Python for AI
• Lesson 3: Writing Simple AI Algorithms
• Lesson 4: Introduction to AI Libraries (NumPy, Pandas)
• Lesson 5: Overview of AI APIs

• Lesson 1: Adversarial Search and Game Trees
• Lesson 2: Minimax Algorithm
• Lesson 3: Alpha-Beta Pruning
• Lesson 4: Monte Carlo Tree Search
• Lesson 5: Applications in Game AI

• Lesson 1: Non-Monotonic Reasoning
• Lesson 2: Default Logic
• Lesson 3: Description Logics
• Lesson 4: Reasoning about Uncertainty
• Lesson 5: Temporal and Spatial Reasoning

• Lesson 1: Bayesian Networks
• Lesson 2: Markov Networks
• Lesson 3: Hidden Markov Models (HMMs)
• Lesson 4: Probabilistic Graphical Models
• Lesson 5: Applications in Real-World Scenarios

• Lesson 1: Introduction to Fuzzy Logic
• Lesson 2: Fuzzy Sets and Membership Functions
• Lesson 3: Fuzzy Inference Systems
• Lesson 4: Applications of Fuzzy Logic in AI
• Lesson 5: Fuzzy Logic vs Probabilistic Methods

• Lesson 1: Bias in AI Models
• Lesson 2: Fairness and Transparency
• Lesson 3: Accountability in AI Systems
• Lesson 4: AI Regulation and Policies
• Lesson 5: Case Studies on Ethical Issues

• Lesson 1: Overview of Generative AI
• Lesson 2: Generative Adversarial Networks (GANs)
• Lesson 3: Diffusion Models
• Lesson 4: AI in Art and Creativity
• Lesson 5: Applications and Challenges of Generative AI

• Lesson 1: What is Explainable AI?
• Lesson 2: Techniques for XAI
• Lesson 3: Interpretable Machine Learning Models
• Lesson 4: Applications of XAI in Critical Fields
• Lesson 5: Challenges in Achieving XAI

• Lesson 1: AI-Driven Robot Control
• Lesson 2: AI in Multi-Robot Systems
• Lesson 3: Collaborative Robotics
• Lesson 4: AI for Autonomous Vehicles
• Lesson 5: Cutting-Edge Robotics Applications

• Lesson 1: AI in Healthcare
• Lesson 2: AI for Climate Change
• Lesson 3: AI in Education
• Lesson 4: AI in Disaster Response
• Lesson 5: Challenges in Scaling AI for Social Good

• Lesson 1: Introduction to Agentic AI Systems
• Lesson 2: Architecture of Agentic AI Systems
• Lesson 3: Autonomy and Proactivity in AI
• Lesson 4: Ethical Considerations in Agentic AI
• Lesson 5: Future Directions in Agentic AI

• Lesson 1: Introduction to Multimodal & Multisensory AI
• Lesson 2: Key Modalities in Multimodal AI
• Lesson 3: Fusion Techniques in Multimodal AI
• Lesson 4: Multisensory Integration
• Lesson 5: Challenges and Future Directions in Multimodal & Multisensory AI

• Lesson 1: Introduction to AI Security
• Lesson 2: Types of Adversarial Attacks
• Lesson 3: Defense Mechanisms Against Adversarial Attacks
• Lesson 4: Securing AI Models and Pipelines
• Lesson 5: Future of AI Security

• Lesson 1: Current Trends in AI Research
• Lesson 2: Artificial General Intelligence (AGI)
• Lesson 3: AI in Quantum Computing
• Lesson 4: Challenges in Building AGI
• Lesson 5: Preparing for the AI-Driven Future

Course Duration: about 50+10 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 Artificial Intelligence and we will accomplish a real-world project together for about 10 hours with Python Programming or Matlab software, which fully prepares you to find a job as a entry level AI developer in Artificial Intelligence 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