Hasso-Plattner-Institut20 Jahre HPI
Hasso-Plattner-Institut20 Jahre HPI
  
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Machine Learning in Precision Medicine (Sommersemester 2019)

Dozent: 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)

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.04.2019 bis 26.04.2019
  • Lehrform: Vorlesung / Seminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch

Studiengänge & Module

Digital Health MA
  • SCAD-Concepts and Methods
  • SCAD-Technologies and Tools
  • SCAD-Specialization
  • APAD-Concepts and Methods
  • APAD-Technologies and Tools
  • APAD-Specialization
IT-Systems Engineering MA
Data Engineering MA

Beschreibung

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

 

16/07/2019

Final Exam

 

supervised learning

 

unsupervised learning

Leistungserfassung

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.

Termine

  • 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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