Courses
Practicum AI is a hands-on applied Artificial Intelligence learning curriculum intended for beginners in AI with limited coding and math background. Our approach is to use visual and conceptual explanations, coupled with hands-on coding exercises to lead learners through a progression of content from beginner to more advanced. AI is not just for computer scientists and we seek to broaden diversity of both AI practitioners and the domains where AI is applied. Our content is open source and freely available for learners as well as instructors who may want to adapt modules for their own teaching. For those wishing to obtain certificates, the content will also soon be available via UF Professional and Workforce Development.
Getting Started
Introduction to Artificial Intelligence and Practicum AI Welcome! This is the place to start your AI journey. Here we introduce you to artificial intelligence–it’s a term we hear a lot, but can you define it? Did you know that AI research dates ...
Computing for AI
Welcome to the Practicum AI: Computing for AI Course! This course is the second in the Practicum AI beginner series. This course can also be taken on its own to familiarize yourself with some important tools used in computational science applicat...
Python for AI
Artificial Intelligence has advanced a lot, but still requires human input. Humans must still prepare the data, setup and train the models, and interpret the results. These steps, while increasingly assisted by AI itself, require some understandin...
Deep Learning Foundations
Deep learning is the focus of modern AI. Models have many layers and millions, or now approaching a trillion, parameters! This course breaks things down and introduces you to a small AI model to provide a conceptual understanding of how AI models ...
Convolutional Neural Networks
Computer vision with Convolutional Neural Networks is the best place to continue your AI learning journey. This course, intended to be the first in our intermediate series of courses, builds on the the Deep Learning Foundations course and builds ...
Transformers
Initially applied to Natural Language Processing (NLP), transformers transformed 😉 natural language processing and have continued to find new applications. For an introduction to NLP, check out our NLP course. Access content Link...
Generative Networks
This series of modules introduces learners to generative adversarial networks (GANs). GANs work with two networks, one trained to produce fake output trying to make output that the second network cannot distinguish from real output. Music and imag...
Transfer Learning
The time and resources that go into training large AI models is significant. Often it is advantageous to re-use a pre-existing model, adapting it for your own purposes. This course covers transfer learning, where a pre-trained model is used as a s...
Natural Language Processing
This series of modules introduces learners to natural language processing (NLP). NLP is used in everything from recommender systems that suggest related products when you shop online to automated translation and speech to text. In this series, we...
Recurrent Networks
This series of modules introduces learners to recurrent neural networks (RNNs). RNNs are used in analyzing time series data, where knowledge of the state at previous time points is helpful in predicting a future time point.Applications include for...
FAIR Data for AI
This module covers making data for AI/ML FAIR (Findable, Accessible, Interoperable, and Reusable). Development of these FAIR AI/ML training modules was supported by a supplement to NIH/NIGMS T34GM118272 (D. Julian, PI; 08/01/2021-05/31/2023). ...
Learning Paths
While each course stands on its own, we recommend following the sequencing, especially for the beginner series.
Practicum AI Beginner Series
Practicum AI Intermediate Series
