This course introduces students to artificial intelligence, providing essential tools for analyzing and solving complex real-world problems. The material spans machine learning, neural networks, language modeling, intelligent search, reinforcement learning, and reasoning. By the end of the semester, students will have gained a broad set of tools and models that form the basis for AI applications and practices.
The course emphasizes both basic concepts relevant to AI and also the practical know-hows. Students will practice through mathematical and programming assignments to study AI, project proposal and final project presentations. The tentative list of topics includes:
Prerequisites: We will use various aspects of linear algebra and probability when needed during the course. When we talk about a specific concept, we will cover the ground up. We will use python as the main programming language when it comes to coding exercises. We will provide detailed instructions when a relevant tool is required as part of a homework problem.
Week 1, Sep 4: Course introduction
Course plan, foundation and history of AI.
Week 2, Sep 8: Supervised learning
Linear regression and gradient descent.
You are responsible for keeping up with all announcements made in class and for all changes in the schedule that are posted on the Canvas website.
The grade will be based on the following:
Homeworks: 40%
Exam (take-home): 40%
In-class project presentations and participation: 20%
Textbooks for reference:
Artificial Intelligence: A Modern Approach, 4th US ed. Stuart Russell and Peter Norvig.