Learning Outcomes:
Upon completion of the module, students should be able to:
1. Identify suitable machine learning / deep learning approaches given a specific task / application.
2. Design and implement data analytics pipeline using Python libraries using industry adopted platforms.
3. Critically evaluate the performance of machine learning / deep learning models.
Indicative Module Content:
a) Medical imaging analysis (Deep Learning for computer vision with various image types)
b) Multi-Omics - e.g., gene expression inference, analysis of human splicing codes - determination of disease, prediction of non-coding variant, etc.
c) Protein structure prediction - e.g., prediction of protein secondary structure, prediction of protein contact map, etc.
At the end of the module students will be able to implement the various techniques of ML/DL on the specific application themes such as those mentioned above. Students will explore the latest state-of-the-art models running on commercial platforms (e.g., TensorFlow on google).