You will learn about how deep learning relates to AI and machine learning. You will learn about neural networks and its variants, and applications in bio-imaging, and the -omics. You will learn about the limitations of deep learning and gain some practical experience.

Aims and objectives

1. You will learn about how deep learning and biomedical data are coevolving together
2. You will learn about basic architectures and algorithms in deep learning
3. You will learn how deep learning is applied in various biomedical settings

Syllabus

1. An introduction to Machine Learning and Artificial Neural Networks
2. Convolutional Neural Networks
3. Recursive Neural Networks
4. Deep Learning in Biomedical Imaging
5. Deep Learning in Genomics and Transcriptomics
6. Deep Learning in Proteomics
7. Limitations and Problems in Deep Learning
8. Hands-on Part 1
9. Hands-on Part 2

Assessment

Respondus CA (Multiple response questions and/or structured short answer questions) (1 per lecture, worth 10% each) ​​	Individual	70%
Group Project (3 weeks)	Group	30%
		100%