Artificial Intelligence: Full Course(AI001)

Source: Dev.to

Foundations of Artificial Intelligence (PART 1)

• What is Artificial Intelligence? link
• History and evolution of AI link
• AI vs ML vs DL vs Data Science
• Types of AI: Narrow, General, Super AI
• Intelligent agents and environments
• Rationality, autonomy, and learning
• AI problem‑solving mindset

Problem Solving & Search (PART 2)

• State space representation
• Uninformed search: BFS, DFS, IDS, UCS
• Informed search: Greedy, A*
• Heuristics: admissibility and consistency
• Game playing and adversarial search
• Minimax algorithm
• Alpha‑beta pruning
• Constraint Satisfaction Problems (CSPs)
• Backtracking and constraint propagation

Knowledge Representation & Reasoning (PART 3)

• Propositional logic
• First‑order predicate logic
• Inference and deduction
• Resolution and unification
• Knowledge bases
• Rule‑based systems
• Semantic networks
• Frames and ontologies
• Description logic
• Reasoning under uncertainty

Planning & Decision Making (PART 4)

• Classical planning
• STRIPS representation
• Forward vs backward planning
• Planning graphs
• Decision theory basics
• Utility theory
• Markov Decision Processes (MDPs)
• Policy and value iteration
• Partially Observable MDPs (POMDPs)

Probability & Uncertainty in AI (PART 5)

• Probability theory for AI
• Bayesian inference
• Bayes networks
• Conditional independence
• Inference in Bayesian networks
• Hidden Markov Models (HMMs)
• Kalman filters
• Particle filters
• Handling noise and uncertainty

Machine Learning (AI Core) (PART 6)

• Learning paradigms overview
• Supervised learning
• Unsupervised learning
• Semi‑supervised learning
• Reinforcement learning
• Bias–variance trade‑off
• Model evaluation and validation
• Overfitting and regularization

Classical Machine Learning Algorithms (PART 7)

• Linear and logistic regression
• k‑Nearest Neighbors
• Naive Bayes
• Decision trees
• Ensemble methods
• Support Vector Machines
• Clustering algorithms
• Dimensionality reduction

Neural Networks & Deep Learning (PART 8)

• Artificial neural networks
• Perceptron and multilayer networks
• Activation functions
• Backpropagation
• Optimization techniques
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• LSTM and GRU
• Attention mechanism
• Transformers
• Large Language Models (LLMs)

Reinforcement Learning (PART 9)

• Reinforcement learning fundamentals
• Agent–environment interaction
• Reward design
• Value‑based methods
• Policy‑based methods
• Q‑learning
• SARSA
• Deep Reinforcement Learning
• Exploration vs exploitation

Natural Language Processing (PART 10)

• Language modeling
• Text preprocessing
• Word embeddings
• Sequence‑to‑sequence models
• Attention in NLP
• Transformers for NLP
• LLMs and chat systems
• Evaluation of NLP systems

Computer Vision (PART 11)

• Image representation
• Feature extraction
• CNNs for vision
• Object detection
• Image segmentation
• Face recognition
• Vision transformers
• Multimodal learning

Explainability, Ethics & Fairness (PART 12)

• Explainable AI (XAI)
• Interpretability vs accuracy
• Bias in AI systems
• Fairness metrics
• Ethical AI principles
• Privacy and security
• Responsible AI

AI Systems & Deployment (PART 13)

• AI pipelines
• Data engineering for AI
• Model deployment
• Monitoring and drift detection
• Scaling AI systems
• Edge AI
• AI in production failures

Applications of AI (PART 14)

• AI in healthcare
• AI in finance
• AI in recommendation systems
• AI in autonomous systems
• AI in robotics
• AI in sports analytics
• AI in education

Advanced & Future AI (PART 15)

• Causal AI
• Neuro‑symbolic AI
• Self‑supervised learning
• Multimodal foundation models
• AGI research
• AI alignment and safety
• Future of AI research

AI Research & Career (PART 16)

• How to read AI research papers
• Experimental design in AI
• Benchmarks and datasets
• Reproducibility in AI
• AI engineer vs AI researcher
• Building impactful AI projects