Generative AI

Course Overview and Objectives:

This course provides students with a comprehensive introduction to the fundamentals of Generative Artificial Intelligence (GenAI), with a particular focus on image synthesis. It is designed to cultivate both theoretical understanding and practical implementation skills, while encouraging students to critically examine the applications of GenAI across diverse domains. By engaging with state-of-the-art models and techniques, students will explore the current limitations and challenges of GenAI, as well as emerging research trends and potential future directions in image generation.

To strengthen the connection between theory and practice, the course features joint instruction with Dr. Chia-Min Cheng, Senior Expert from AI Technology Division of MediaTek, who will share cutting-edge industry insights. His lectures will bridge academic concepts with real-world product development, highlighting both technical challenges and commercial opportunities.

• Acquire foundational knowledge and technical skills in generative AI and image synthesis.
• Demonstrate sensitivity to emerging technologies and the ability to critically analyze new trends.
• Conduct independent research and development projects, while also contributing effectively to team collaboration and project management.
• Apply innovative thinking and problem-solving skills to advance technological innovation and create broader societal impact.

Prerequisites

• Programming Proficiency in Python
  • All assignments will be implemented in Python. Students with extensive programming experience in other languages (e.g., C++, Matlab, or JavaScript) should still be able to adapt.
• Mathematics Foundations
  • Calculus and Linear Algebra: Ability to take derivatives, and familiarity with matrix–vector operations and notation.
  • Probability and Statistics: Understanding of basic probability concepts, Gaussian distributions, mean, standard deviation, and related statistical measures.
• Deep Learning/Machine Learning

Grading

• Assignments (20%): Including programming assignments, literature review reports, etc.
• Midterm Report (30%): Students need to select a GenAI-related paper from the past three years and write a research report. The report should include Background, Motivation, Proposed Method, Result, and Personal Reflection. Additional points will be awarded for including a demo and technical implementation in the report.
• Final Project (40%): Including project presentation, and final project report. Students are required to select a GenAI-related topic, develop a proposal, and undertake an implementation project. The evaluation criteria encompass a thorough comprehension of the problem, innovation, practicality of the solution, completeness, and performance of the technical implementation. Additional credit will be awarded for incorporating a demo and technical implementation.
• Class Participation (10%): Class Participation.
• For each absence during roll call:-1 for the final grade.

Office Hours

• Monday 11:00-12:00 am
• Room: Engineering Building-6 (374)

Progress