Deep Neural Networks is an emerging technology, it has become ubiquitous in many areas of applications. The performance of Deep Neural Networks beats many more traditional machine learning methodologies. This course will provide students with the foundations of Deep Learning to enable them to develop and use Deep Learning methodologies in their career. The students will be able to understand enough principles to choose the most suitable Deep Learning tools for their tasks as well as to trouble shoot and interpret their results.

Aims and objectives

This course provides students the foundations of Deep Learning to enable them to use Deep Learning tools for biomedical applications. The skill sets acquired in this course includes:

1. ability to build Deep Neural Networks independently
2. to understand the principles of Deep Learning so as to use the correct tool for the required tasks
3. ability to trouble shoot and interpret results generated by Deep Learning

Syllabus

1. Foundations of Deep Learning, multilayer perceptrons, loss functions
2. Convolutional Neural Networks
3. Training, validations and testing of neural networks, metric of performance measurements
4. Biomedical applications, analysis of microscopy images, histology slide reading, radiology scans reading, applications in structural biology

Assessment

Project​​	Indivi​​dual	40%
Assignments​	Individual	35%
Quiz(MCQ & short answer question​s)	Individual	25%
		100%