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SE 444: Introduction to Artificial Intelligence

📋 Course Information

Field Details
Course Code SE 444
Course Title Introduction to Artificial Intelligence
Credits 3
Prerequisites SE 255 (Data Structures), STAT 201 (Probability)
Semester Fall 2025
Instructor Prof. Anis Koubaa
Institution Alfaisal University, College of Engineering

🎯 Course Description

In this course, students will learn the foundational principles that drive AI applications and practice implementing AI-enabled systems. Topics include intelligent agents, search methods, game playing, constraint satisfaction, logical reasoning, planning, probabilistic reasoning, Markov decision processes, and game theory.

📋 Learning Outcomes (CLOs)

🧠 Knowledge & Understanding

  • CLO 1.1: Describe and identify the basic concepts of artificial intelligence, fundamental problem-solving and inference approaches, techniques for exploiting regularity in data, and computational theories related to human intelligence

⚙️ Skills

  • CLO 2.1: Assess and evaluate the design and operation of AI models
  • CLO 2.2: Apply search methods and techniques to solve AI problems

🤝 Values, Autonomy, and Responsibility

  • CLO 3.1: Work cooperatively and effectively as member/leader of a development team to deliver quality systems

📖 Required Textbooks

Type Title Author
Primary Artificial Intelligence: A Modern Approach (4th Ed) Stuart Russell & Peter Norvig
Secondary Artificial Intelligence (3rd Edition) Patrick Henry Winston

📅 Course Schedule Overview

Phase 1: Foundations & Search (Weeks 1-6)

Week Topic Assessment
1 Introduction to AI, Intelligent Agents —
2-3 Search Algorithms (BFS, DFS, A*, UCS) Quiz 1
4 Local Search & Optimization —
5 Adversarial Search & Games —
6 Constraint Satisfaction Problems Midterm 1

Phase 2: Logic & Planning (Weeks 7-11)

Week Topic Assessment
7 Propositional Logic —
8 First-Order Logic —
9 Automated Planning Quiz 2
10 Reasoning Under Uncertainty —
11 Bayesian Networks Midterm 2

Phase 3: Advanced Topics (Weeks 12-17)

Week Topic Assessment
12 Markov Decision Processes Quiz 3
13 Game Theory & Nash Equilibrium —
14 Mid-Semester Break —
15 AI Ethics, Privacy, Safety Project Proposal
16 Project Development —
17 Final Presentations Final Project

📊 Assessment Structure

Component Weight Description
Midterm 1 15% Weeks 1-6: Agents, Search, CSP
Midterm 2 15% Weeks 7-11: Logic, Planning, Probability
Final Exam 30% Comprehensive
Quizzes (3) 15% Quiz 1 (Search), Quiz 2 (Logic), Quiz 3 (Bayesian)
Project 15% RoboMind AI agent project
Assignments 10% Homework assignments

Total: 100%

📚 Lecture Index

# Topic Slides
L00 Course Overview 1
L01 Introduction to AI 1
L02 Intelligent Agents 1
L03 Search Algorithms 1
L04 Local Search 1
L05 Adversarial Search & Games 1
L06 Constraint Satisfaction Problems 1
L07 Propositional Logic 5
L09 Planning 2
L10 Reasoning Under Uncertainty 2
L11 Bayesian Networks 3
L12 Markov Decision Processes 2
L13 Game Theory & Nash Equilibrium 2

🛠️ Course Resources

  • E-Learning Platform: Alfaisal Moodle
  • Course Repository: GitHub repository with code examples
  • Laboratory Sessions: Hands-on coding exercises
  • Office Hours: By appointment
  • Web Demos: Interactive algorithm visualizations

📝 Related Documents

Part of SE 444: Introduction to Artificial Intelligence - Alfaisal University
Last Updated: January 2025