Artificial Intelligence Foundations: Logic, Learning, and Beyond Course Syllabus

Overview: This course provides a comprehensive introduction to artificial intelligence, blending theoretical foundations with hands-on programming to build core competencies in search, logic, learning, and planning. Structured into eight modules, learners will spend approximately 8 weeks completing the course with a balanced mix of lectures, coding exercises, and real-world applications. Each module spans roughly one week, requiring 6–8 hours of engagement, including reading, coding practice, and problem-solving. By the end, learners will have implemented key AI algorithms in Python, evaluated models using proper metrics, and gained awareness of ethical implications—laying a strong foundation for advanced AI studies or entry-level roles in AI-driven industries.

Module 1: Foundations of AI

Estimated time: 7 hours

• History of AI and key milestones
• Turing Test and machine intelligence
• Rational agents and their design
• PEAS framework for task environments

Module 2: Problem Solving & Search

Estimated time: 7 hours

• Uninformed search algorithms: BFS and DFS
• Informed search with heuristics
• A* search algorithm and optimality
• Pathfinding problems on grid worlds

Module 3: Knowledge Representation & Logic

Estimated time: 7 hours

• Propositional logic syntax and semantics
• First-order logic basics
• Inference and resolution methods
• Encoding puzzles using logical representations

Module 4: Planning & Decision Making

Estimated time: 7 hours

• STRIPS for action representation
• Forward and backward planning
• Markov Decision Processes (MDPs)
• Value iteration in grid-world environments

Module 5: Machine Learning Basics

Estimated time: 7 hours

• Supervised learning with linear and logistic regression
• Decision trees and model interpretation
• Overfitting and bias-variance tradeoff
• Model evaluation using cross-validation

Module 6: Unsupervised Learning & Clustering

Estimated time: 7 hours

• K-means clustering algorithm
• Hierarchical clustering techniques
• Dimensionality reduction with PCA
• Visualizing clustered data projections

Module 7: Neural Networks & Deep Learning Intro

Estimated time: 7 hours

• Perceptron and activation functions
• Multilayer neural networks
• Backpropagation algorithm
• Deep learning basics with MNIST classification

Module 8: Reinforcement Learning Basics

Estimated time: 7 hours

• Exploration vs. exploitation dilemma
• Q-learning algorithm
• Policy gradients overview
• Implementing agents in OpenAI Gym environments

Prerequisites

• Basic Python programming knowledge
• Familiarity with introductory mathematics (linear algebra, probability)
• Comfort with Jupyter notebooks and data libraries (NumPy, pandas)

What You'll Be Able to Do After

• Design rational agents using PEAS frameworks
• Implement and compare search algorithms for problem-solving
• Represent knowledge and reason using logic
• Build and evaluate machine learning models for classification and clustering
• Apply foundational neural networks and reinforcement learning techniques to real-world tasks