Artificial intelligence should be made accessible to all. To this end, we provide a spelled-out introduction to neural networks and their applications for students from all disciplines, regardless of their background or area of study. Starting with the classical problem of image recognition, students will delve into the intricacies of modeling multilayer perceptrons, backpropagation algorithm for optimization, model training and evaluation, as well as extensions and variants of the basic architecture. By empowering students with hands-on modeling and programming skills, we aim to democratize AI technology and foster a community of `AI ninjas' capable of harnessing its transformative potential for innovation, economic growth, and the betterment of humanity.
- Fall 2024 Syllabus
- Location: Davis-Shaughnessy Hall 171
- Time: M 6:00pm-9:15pm
- Office hours: MW 2:00pm-3:00pm & by appointment
- Discord: discord.gg/SsrNPEeP2P
- GitHub Classroom: classroom.github.com
- Lecture 0: Python Tutorial
- Lecture 1: Linear Classification
- Lecture 2: Neural Networks
- Lecture 3: A Minimal Case Study
- Lecture 4: Language Modeling
- More applications to follow.
Note: Adobe Acrobat Reader is required to view animations in these PDF lectures.