Offered: Fall 2024 and Spring 2025
This page contains materials used in the AI Foundations “AI FIrst” course offered at the Georgia Institute of Technology. The course is designed for first year college and high school students. My goal is to establish AI literacy for all students in all high schools and colleges across the State of Georgia. Students will gain experience and practice with algorithm design, data structure, logical thinking, reasoning, explanations, and AI systems. A focus is on developing skills and experience in utilizing the AI Makerspace to perform certain functionalities on real data.
Students will understand the basic concepts of Artificial Intelligence, build on the basic concepts of computer programming in a high-level language, understand the principles of data literacy, understand the math principles for AI, understand the process and skills necessary to effectively deal with problem solving in relation to constructing AI algorithms, be able to test, assess, and evaluate AI models and systems, understand the societal and ethical implications of AI systems, and become comfortable with common software packages in use nowadays.
For inquiries, contact Prof. Ghassan AlRegib.
Course Objective
An introduction to the Foundations of AI.
Textbook
In print. (will be published)
Assignments
Assignments: Assignments are split between homeworks and lab reports. Students will utilize the AI Makerspace for all labs during this course. These hands-on labs vary between adding to existing codes, writing codes from scratch, searching the literature for codes for a specific application, or summarizing papers in the literature. The final exam is a project where students use the knowledge they acquire during the semester to create working product.
Programming Language
Programming Language: We will utilize Python throughout the course. We will also utilize a library of Jupyter notebooks in Georgia Tech’s AI Makerspace.
Topical Outline
- What is AI?
- AI Problem Solving
- Real-world AI Systems and Applications
- Hardware and GPUs
- Data:
- Attributes
- Structures
- Entropy
- Greedy Algorithms
- SIM measures and Distances
- Representations
- Transforms
- Dimensionality Reduction Algorithms, PCA
- Knowledge, Logic, and Reasoning
- Logical operations, Rules of Inference, Logical Consequences, Semantics of Expression, Reasoning Patterns, Interpretations and Inferences, Rule-based reasoning, Forward and Backward Chains, Recursive functions, problem reduction, AND-OR tree, Logic foundation, Biconditional and Boolean operations
- Ethics
- Case studies, Issues and Challenges, Policy making and governance, bias, fairness and trust in AI, uncertainty, adversarial challenges, transparency and explainability, implications on society and individuals
- Learning
- Regression
- Gradient Descent
- Regularization
- Performance Evaluations
- Model Validation
- SVM
- Unsupervised methods, Clustering
- ANNs
- Back Propagation
- CNNs
- Best Practices
- XAI
- Search, Decision and Planning
- mini-max, Adversarial search, mathematical programming, resource allocation using LP, risk/reward optimization using QP, sequential decision making, path planning, dynamic programming, Reinforcement learning
- Advanced Topics:
- Autoencoders
- VAEs
- Transformers
- LLMs
- Anomaly Detection
- Trustworthy AI
Term Projects
Every semester, students work on a final project such s designing and training their own individual GPT.
Lectures
Any use of the below materials must acknowledge Prof. Ghassan AlRegib and this source. Recordings and notes will be published here.
Recitations
In recitations, instructors will work on solving problems and deepen students’ understanding of the major concepts.
Studios and Labs
Students work on 6~7 studio labs throughout the semester. Al studios will be conducted on the AI Makerspace. Several tools and services are developed by the VIP team to facilitate this part of the course.
Acknowledgment
The following collaborators have been an integral part of the course development at various stages of the course development from 2023 through 2025:
Kiran P. Kokilepersaud
Seulgi Kim
The following Teaching Assistants (TAs and UTAs ) have done remarkable job during their assignment to the course:
Julia Bargouti
Rodrigo Gaeta Lopez
Sitong Li
William Wade
Min Han
