Introduction to machine learning in biomedical research - Part A

May 23-31, 2022

PANUM, Blegdamsvej 3B, 2200 Copenhagen, Maersk Tower, Floor 15, room 7.15.152

Course plan

Main teacher: Shyam Gopalakrishnan fck245@ku.dk

Introduction to ML

Time
9-10	Introduction to Machine Learning
10-11	Unsupervised learning: PCA
11-12	PCA exercises
12-13	Lunch break
13-14	Supervised learning: classification and regression
14-15	Logistic regression exercises

Main teacher: Anders Krogh anders.krogh@sund.ku.dk

Neural Networks

Time
9-10	Lecture: What is a NN? Feed-forward NN. Backpropagation. Day2/NNintro1.pdf. Notebook for gradient descent
10-11	Exercise: Introduction to Pytorch. Make your first NN
11-12	Lecture: Issues in training. SGD, Adam. mixed with hands-on. Day2/NNintro2.pdf
12-13	Lunch break
13-14	Exercise with gene expression data
14-15	Lecture: Neural networks for sequences (one-hot encoding, convolution). Performance evaluation (ROC curve). Day2/NNintro3.pdf
15-16	Exercise on prediction of TSS in DNA sequences

Main Teacher: Ole Winther ole.winther@bio.ku.dk (morning) Anders (afternoon)

Generative Neural Networks

Time
9-10	Generative Neural Networks
10-12	Exercise on Generative Neural Networks
12-13	Lunch break
13-14	Start project work. See project folder.
14-15	Talk by Mani Arumugam: Gut microbiome signatures of Colorectal Cancer. (Data set for use in the Hackathon)
15-16	Continue project

Main Teacher: Anders

Time
9-12	Continue project work
12-13	Lunch break
13-15	Project presentations
15-16	Reflections, Discussion, Questions, Evaluation

Talks: Example of machine learning applications in the biomedical field & intro to hackathon

Please visit the following links for details on the three datasets for next week's course, part-B:

For specific questions about the Chromatin dataset (P0001), please send your questions to: christian.gronbaek@sund.ku.dk