Artificial Intelligence Bootcamp
This short course provides an overview of the field of Artificial Intelligence (AI) with emphasis on contemporary techniques, such as machine learning and deep learning, and their applications in many areas, including computer vision, natural language processing, and medical diagnosis.
Students will learn the basics of AI, machine learning, and deep learning, and interact with hands-on examples of applications of AI in numerous domains. The course will broaden the participants' view of the field of AI, allowing a better understanding of its foundations, risks, applications, and implications, and motivating students to learn more about the topic.
Session Length: Next Session Fall 2023
***If you have FAU Email, please REGISTER USING YOUR FAU EMAIL (The course is accessed through Canvas). For those with no FAU email, a user account will be created.
Please note that each class will be recorded. The recordings will be available online for 30 days to ensure that participants can access them if they miss a class or want to review any of the sessions.
Length: One week of online sessions (Monday-Friday, 3 hours/day)
Cost: $250 Non-FAU Participants -
Available to FAU Student/Faculty/Staff Only
*Please contact Bootcamp Coordinator to receive your discount code prior to registering.
Instructor: Oge Marques, PhD
Tools and Resources:
Lectures and group discussions will take place live (synchronously) online (using Zoom) on specified days and times.
Slides and supporting materials will be posted online.
Coding examples will be presented using MATLAB Online. Bootcamp participants will receive a complimentary 30-day license to access MATLAB Online and selected toolboxes (courtesy of MathWorks).
Students should have reliable Internet access to watch the live lectures and access MATLAB Online and work on associated examples.
Outline and tentative schedule
The course will consist of 5 modules (of 3 hours each). Each module will have a combination of lecture, demos, and discussions, with ample opportunity for questions.
- Fundamentals of AI: history, techniques, applications
- Fundamentals of Machine Learning (ML) and Deep Learning (DL)
- Examples of latest developments in AI, ML and DL
- The Machine Learning workflow: from data acquisition to deployment of a solution
- Example of a ML workflow
- Neural Networks: fundamentals and examples (using TensorFlow playground or equivalent)
- Deep Learning architectures (CNNs, RNNs, and more): fundamentals and examples (using Google Colab notebooks)
- Transfer Learning
- Deep Learning examples in computer vision, natural language processing, and medical diagnosis.
- AI and DL beyond the code: adversarial examples, transparency, fairness, bias explainability (the “black box effect”), data sharing, model sharing,accountability, and more.
- Where to go from here: suggestions of courses to take at FAU, books,newsletters, YouTube channels, podcasts, and other resources.