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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.

Important Notice:
 We regret to inform you that the online class originally scheduled to take place on August 7 - 11 has been canceled due to unforeseen circumstances beyond our control.

We understand the disappointment this may bring, and we sincerely apologize for any inconvenience caused.

Stay tuned for updates on future courses, bootcamps, and events by regularly visiting our website and following our social media channels.


Course Outline

Length: One week of online sessions (Monday-Friday, 3 hours/day)

Cost: N/A

Time: N/A

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 Google Colab.

Students should have reliable Internet access to watch the live lectures and a Google account to access the Google Colab notebooks containing 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.

Module 1

  • Fundamentals of AI: history, techniques, applications
  • Fundamentals of Machine Learning (ML) and Deep Learning (DL)
  • Examples of latest developments in AI, ML and DL

Module 2

  • The Machine Learning workflow: from data acquisition to deployment of a solution
  • Example of a ML workflow
  • Neural Networks: fundamentals and examples

Module 3

  • Deep Learning architectures (CNNs, RNNs, and more): fundamentals and examples
  • Transfer Learning
  • Deep Learning examples in computer vision, natural language processing, and medical diagnosis

Module 4

  • Latest trends in AI:
    • Large Language Models (LLMs), such as Chat-GPT and Bard
    • Generative AI, such as Midjourney and DallE-2
    • Vision and language combined
    • Artificial General Intelligence (AGI): are we there yet?

Module 5

  • 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.