Credits: 1 | Time: Tuesdays at 3:30 | Location: Science Ed & Research Ctr, Rm. 00214

Lecture Videos

Available on YouTube (Link).

Prerequisites:

Familiarity with programming concepts (Python experience is helpful but not required).
Understanding of computer science fundamentals (e.g., data structures, algorithms).
Prior knowledge of AI is not required.

Course Overview:

This course offers an introduction to artificial intelligence (AI), emphasizing practical applications across various domains. Students will explore key AI concepts without delving deeply into the technical details or underlying mathematics. The primary focus will be on understanding how AI solves real-world problems. Throughout the course, students will complete small assignments and projects to apply AI concepts to real-world challenges.

The course culminates in a capstone project, where students can choose to implement an AI solution, design one without implementation, or analyze existing AI models and their applications.

Learning Objectives

Develop a foundational understanding of AI and its practical applications.
Analyze how AI solves real-world problems and recognize its strengths and limitations.
Interpret existing AI code implementations from sources like Kaggle and Hugging Face.
Identify opportunities for leveraging AI in various domains.
Design and present a small-scale AI-powered solution for a real-world challenge.

Course Structure

See the Syllabus page for course structure and the slides.

Capstone Project

Students may choose to:

Implement an AI solution or
Ideate and design an AI solution without implementation
Analyze existing AI models or code from platforms like Kaggle, focusing on understanding their applications and limitations.
Investigate a challenge or an opportunity that AI presents to their field of interest.

Capstone Project Evaluation Criteria:

Problem Identification (20%): Clear articulation of the problem and AI’s role in solving it.
AI Application (20%): Demonstration of how AI could solve the problem.
Data Handling and Feature Engineering (20%): Effective use of data and feature engineering techniques.
Evaluation and Results (10%): Assessment of the AI solutions.
Limitation (%10): Understand the limitations of their AI solution
Final Presentation (20%): Clear, concise summary of the project

Grading:

Capstone Project: 50%
Assignments and Participation: 50%