Course Description
This course provides a comprehensive introduction to artificial intelligence, covering fundamental concepts, algorithms, and applications. Students will learn about search algorithms, knowledge representation, reasoning, machine learning, and neural networks.
Learning Objectives
By the end of this course, students will be able to:
- Understand the history and foundations of AI
- Implement search algorithms for problem-solving
- Apply machine learning techniques to real-world problems
- Design and train neural networks
- Evaluate AI systems ethically and critically
Prerequisites
- CS201: Data Structures and Algorithms
- MATH301: Linear Algebra
- Basic programming experience in Python
Evaluation
| Component | Weight |
|---|---|
| Assignments (4) | 30% |
| Midterm Exam | 25% |
| Final Project | 30% |
| Class Participation | 15% |
Schedule
| Week | Topic | Reading |
|---|---|---|
| 1 | Introduction to AI | Chapter 1 |
| 2-3 | Search Algorithms | Chapters 3-4 |
| 4-5 | Knowledge Representation | Chapters 7-8 |
| 6-7 | Machine Learning Basics | Chapters 18-19 |
| 8 | Midterm Exam | - |
| 9-10 | Neural Networks | Chapter 21 |
| 11-12 | Deep Learning | Supplementary |
| 13-14 | AI Applications & Ethics | Chapter 27 |
| 15 | Project Presentations | - |
Resources
- Textbook: Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th Edition
- Software: Python 3.10+, PyTorch, Jupyter Notebooks
- Online: Course materials available on Canvas