Course Outline
NBIT 405: Artificial Intelligence (AI) - From AI Foundation to Large Language Models
A. AI Foundations
- Landscape of Hardwares, Softwares and Data Modalities for AI
- AI Accelerators
- AI Frameworks and Libraries
- Multimodal Data
- Computational Linear Algebra and Differentiable Programming
- Foundational Probablility and Information Theory
- Fundamentals of Deep Neural Network
- Backpropagation
- CNN
- RNN
B. Large Languagee Models
Referrence Books on Mathematics and Satistics for Machine Learning and Deep Learning and NLP/LLMs
Books on Calculus, Linear Alegebra, Probability and Statistics
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- The Elements of Differentiable Programming by Mathieu Blondel and Vincent Roulet
- Introduction to Probability for Data Science by Stanley Chan
- Probability and Statistics for Data Science by Carlos Fernandez-Granda
- Information Theory for Data Science by Changho Suh
Books on Deep Learning
- Deep Learning by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author)
- Understanding Deep Learning by Simon J.D. Prince (Author)
- Deep Learning: Foundations and Concepts by Christopher M. Bishop (Author), Hugh Bishop (Author)
- Dive into Deep Learning || Dive into Deep Learning 1st Edition by Aston Zhang (Author), Zachary C. Lipton (Author), Mu Li (Author), Alexander J. Smola (Author)
- Deep Generative Modeling by Jakub M. Tomczak
- Foundations of Computer Vision by Phillip Isola and William T. Freeman (Interested in Chapter 9-14, 24-26, and 32-34)
Books on NLP and LLMs