Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are playing a significant role in reshaping biomedical sciences including oncology, with applications rapidly growing in areas of diagnosis, prognosis, treatment response modeling, organ segmentation, image-guidance and more.

The AI/ML community focus has been to date on developing new algorithms and solving technical problems related to the application of these algorithms. However, the success of the application of AI/ML algorithms, especially in the clinical domain, hinges on the availability and quality of data for the training and validation of the AI/ML models. Therefore, an unmet need is in the development of competencies and skills needed to make biomedical data ready for AI/ML applications that can meet the four FAIR requirements: Findable, Accessible, Interoperable and Reusable.

In this short course with hands-on training, several topics related to AI/ML ideas and methods along with FAIR data readiness will be introduced to the students (8 weeks). This course is directed by Dr. Issam El-Naqa, chair of Moffitt's Department of Machine Learning. Six of the sessions were recorded and are available to the public.

Available lectures:

Introduction to AI/ML/DL algorithms - Dr. Issam El-Naqa (PDF)
Training Requirements for ML - Dr. Issam El-Naqa (PDF)
Methods Assessment, Uncertainty and Bias Estimation – Dr. Aik Choon Tan (PDF)
FAIR Principles – Dr. Jamie Teer and Dr. Yi Luo (PDF)
Best Practices – Dr. Jamie Teer and Dr. Yi Luo (PDF)
Data Resources - Dr. Aik Choon Tan (PDF)