Machine Learning in Precision Medicine (Sommersemester 2019)

Lecturer: Prof. Dr. Christoph Lippert (Digital Health - Machine Learning) , Jana Fehr (Digital Health - Machine Learning) , Dr. rer. nat. Stefan Konigorski (Digital Health - Machine Learning) , M.Sc. Remo Monti (Digital Health - Machine Learning)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2019 bis 26.04.2019
• Teaching Form: Lecture / Seminar
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

This course is designed to give students an in-depth introduction to machine learning. The lectures and exercises are designed around biomedical use cases and will use real-world biomedical data to gain practical experience with machine learning models and algorithms. The course will start with an introduction to the basic concepts of machine learning and empirical data analysis, such as model fitting, selection and validation. During the second part of the course, we will discuss supervised machine learning, starting with linear models, to non-linear models, including deep neural networks and convolutional neural networks for medical imaging.  During the third part of the course, we will discuss unsupervised learning, starting with clustering, to dimensionality reduction and latent variable models. While we will discuss machine learning in a biomedical context, the learned principles and algorithms are applicable to other fields as well.

Learning Objectives:

• Understand concepts, methods and algorithms in machine learning
• Ability to empirically analyze real-world data
• Ability to assess the quality and validity of a machine learning model for a given analysis
• Ability to select, develop, implement and apply appropriate machine learning models and algorithms for a given use case.
• Gain an introduction to biomedical use cases of machine learning, including clinical prediction problems, medical image analysis, and modeling of multi-omics data.

 

Course Syllabus and Schedule (Summer 2019)

 

Please note that the schedule is still preliminary and details are subject to change.

 

Date	Topic
08/04/2019	Introduction to Machine Learning in Medicine
09/04/2019	Linear Regression
15/04/2019	Regularization and Experimental Design
16/04/2019	Basis Functions
22/04/2019	Ostern
23/04/2019	Link Functions and Logistic Regression
29/04/2019	Linear regression revisited - Probabilistic Interpretation and Bayesian Linear regression
30/04/2019	Feed forward Neural Networks
06/05/2019	Back Propagation
07/05/2019	Stochastic Gradient Descent
13/05/2019	Convolutional Neural Networks
14/05/2019	Medical Imaging 1
20/05/2019	Clustering and k-means
21/05/2019	Gaussian Mixture Models and the Expectation Maximization algorithm
27/05/2019	Principal Components Analysis
28/05/2019	Autoencoders
03/06/2019	Visualization of High-Dimensional Medical Data
04/06/2019	Latent Variable Models and missing data
10/06/2019	Pfingsten
11/06/2019	Expectation Maximization algorithm revisited
17/06/2019	Variational Inference
18/06/2019	Stochastic Variational Inference
24/06/2019	Variational Autoencoder
25/06/2019	Generative Adversarial Networks
01/07/2019	Generative Adversarial Networks
02/07/2019	Medical Imaging 2
08/07/2019
09/07/2019	Open Topics, Final Exam Preparation
15/07/2019
29/07/2019	Final Exam

	supervised learning
	unsupervised learning

Examination

The final grade is based 100% on the final written exam.

Processing of regular exercise sheets (every one to two weeks) is required for a Klausur approval.

Dates

• Lecture #1:  Monday 15:15-16:45
• Lecture #2: Tuesday 9:15-10:45
• Place: Lecture Hall 2 (HPI Campus I / Hörsaalgebäude)
• Tutorials: Time and place will be arranged jointly with students during the first lecture.

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