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Understanding Artificial Intelligence: A Deep Dive
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Course Overview
Day 1: Introduction to AI & Search Algorithms
Day 2: Propositional and First-Order Logic
Day 3: Probability and Bayesian Networks
Day 4: Graph Search (A*, BFS, DFS)
Day 5: Constraint Satisfaction Problems (CSPs)
Day 6: Linear & Logistic Regression
Day 7: Naive Bayes and Generative Models
Day 8: SVMs and Kernel Methods
Day 9: Bias-Variance Tradeoff
Day 10: Clustering, PCA & EM Algorithm
Day 11: Neural Networks & Backpropagation
Day 12: CNNs, RNNs, and Regularization
Day 13: Reinforcement Learning (MDP)
Day 14: Policy Search and POMDPs
Day 15: Lab – RL Agent or Neural Net Training
Day 16: Introduction to Explainable AI (XAI)
Day 17: Interpretability and Fairness
Day 18: Responsible AI and Governance Frameworks
Day 19: Annotated History of AI
Day 20: Final Project Planning and Review
Evaluation