Artificial Intelligence: Full Course(AI001)

PART 1: Foundations of Artificial Intelligence

1. What is Artificial Intelligence? link
2. History and evolution of AI link
3. AI vs ML vs DL vs Data Science link
4. Types of AI: Narrow, General, Super AI link
5. Intelligent agents and environments link
6. Rationality, autonomy, and learning link
7. AI problem-solving mindset link

PART 2: Problem Solving & Search

1. State space representation
2. Uninformed search: BFS, DFS, IDS, UCS
3. Informed search: Greedy, A*
4. Heuristics: admissibility and consistency
5. Game playing and adversarial search
6. Minimax algorithm
7. Alpha-beta pruning
8. Constraint Satisfaction Problems (CSPs)
9. Backtracking and constraint propagation

PART 3: Knowledge Representation & Reasoning

1. Propositional logic
2. First-order predicate logic
3. Inference and deduction
4. Resolution and unification
5. Knowledge bases
6. Rule-based systems
7. Semantic networks
8. Frames and ontologies
9. Description logic
10. Reasoning under uncertainty

PART 4: Planning & Decision Making

1. Classical planning
2. STRIPS representation
3. Forward vs backward planning
4. Planning graphs
5. Decision theory basics
6. Utility theory
7. Markov Decision Processes (MDPs)
8. Policy and value iteration
9. Partially Observable MDPs (POMDPs)

PART 5: Probability & Uncertainty in AI

1. Probability theory for AI
2. Bayesian inference
3. Bayes networks
4. Conditional independence
5. Inference in Bayesian networks
6. Hidden Markov Models (HMMs)
7. Kalman filters
8. Particle filters
9. Handling noise and uncertainty

PART 6: Machine Learning (AI Core)

1. Learning paradigms overview
2. Supervised learning
3. Unsupervised learning
4. Semi-supervised learning
5. Reinforcement learning
6. Bias–variance tradeoff
7. Model evaluation and validation
8. Overfitting and regularization

PART 7: Classical Machine Learning Algorithms

1. Linear and logistic regression
2. k-Nearest Neighbors
3. Naive Bayes
4. Decision trees
5. Ensemble methods
6. Support Vector Machines
7. Clustering algorithms
8. Dimensionality reduction

PART 8: Neural Networks & Deep Learning

1. Artificial neural networks
2. Perceptron and multilayer networks
3. Activation functions
4. Backpropagation
5. Optimization techniques
6. Convolutional Neural Networks (CNNs)
7. Recurrent Neural Networks (RNNs)
8. LSTM and GRU
9. Attention mechanism
10. Transformers
11. Large Language Models (LLMs)

PART 9: Reinforcement Learning

1. Reinforcement learning fundamentals
2. Agent–environment interaction
3. Reward design
4. Value-based methods
5. Policy-based methods
6. Q-learning
7. SARSA
8. Deep Reinforcement Learning
9. Exploration vs exploitation

PART 10: Natural Language Processing

1. Language modeling
2. Text preprocessing
3. Word embeddings
4. Sequence-to-sequence models
5. Attention in NLP
6. Transformers for NLP
7. LLMs and chat systems
8. Evaluation of NLP systems

PART 11: Computer Vision

1. Image representation
2. Feature extraction
3. CNNs for vision
4. Object detection
5. Image segmentation
6. Face recognition
7. Vision transformers
8. Multimodal learning

PART 12: Explainability, Ethics & Fairness

1. Explainable AI (XAI)
2. Interpretability vs accuracy
3. Bias in AI systems
4. Fairness metrics
5. Ethical AI principles
6. Privacy and security
7. Responsible AI

PART 13: AI Systems & Deployment

1. AI pipelines
2. Data engineering for AI
3. Model deployment
4. Monitoring and drift detection
5. Scaling AI systems
6. Edge AI
7. AI in production failures

PART 14: Applications of AI

1. AI in healthcare
2. AI in finance
3. AI in recommendation systems
4. AI in autonomous systems
5. AI in robotics
6. AI in sports analytics
7. AI in education

PART 15: Advanced & Future AI

1. Causal AI
2. Neuro-symbolic AI
3. Self-supervised learning
4. Multimodal foundation models
5. AGI research
6. AI alignment and safety
7. Future of AI research

PART 16: AI Research & Career

1. How to read AI research papers
2. Experimental design in AI
3. Benchmarks and datasets
4. Reproducibility in AI
5. AI engineer vs AI researcher
6. Building impactful AI projects