Artificial Intelligence Foundations: Logic, Learning, and Beyond

Module 1: Foundations of AI

⏳ 1 week

• Topics: History of AI, Turing Test, rational agents, PEAS frameworks

• Hands-on: Define a PEAS description and implement a simple reflex agent in Python

Module 2: Problem Solving & Search

⏳ 1 week

• Topics: Uninformed search (BFS, DFS), informed search (A*, heuristics)

• Hands-on: Build and compare BFS vs. A* on path-finding grids

Module 3: Knowledge Representation & Logic

⏳ 1 week

• Topics: Propositional and first-order logic, inference, resolution

• Hands-on: Encode simple puzzles in propositional logic and solve via resolution

Module 4: Planning & Decision Making

⏳ 1 week

• Topics: STRIPS representation, forward/backward planning, Markov Decision Processes

• Hands-on: Implement value iteration on a grid-world MDP

Module 5: Machine Learning Basics

⏳ 1 week

• Topics: Linear regression, logistic regression, decision trees, overfitting

• Hands-on: Train and evaluate models on a public dataset using scikit-learn

Module 6: Unsupervised Learning & Clustering

⏳ 1 week

• Topics: K-means, hierarchical clustering, dimensionality reduction (PCA)

• Hands-on: Cluster customer data and visualize results with PCA projections

Module 7: Neural Networks & Deep Learning Intro

⏳ 1 week

• Topics: Perceptron, multilayer networks, activation functions, backpropagation

• Hands-on: Build a two-layer neural network from scratch to classify MNIST digits

Module 8: Reinforcement Learning Basics

⏳ 1 week

• Topics: Exploration vs. exploitation, Q-learning, policy gradients overview

• Hands-on: Implement Q-learning for a simple OpenAI Gym environment

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