Machine learning and data science
Machine learning and data science (winter term 2021/2022)
Steffen Rulands
Time: Tuesdays, 4:40pm CET
Location:
Online via Zoom (link)
Meeting ID: 854 1370 2254
Passcode: 090867
Limited physical attendance if regulations allow:
Seminar Room 4
Max Planck Institute for the Physics of Complex Systems
Noethnitzer Str. 38
01187 Dresden
Summary: In recent years, the availability of large sets of data has led to the development of computational methods for their manipulation and interpretation. In this lecture, we will introduce fundamental concepts from data science and machine learning, such as dimensionality reduction, neural networks and deep learning.
Required prior knowledge: Programming
Course materials
Password for PDF files is the last name of the lecturer.
Overview over the lecture - types of machine learning tasks - challenges in machine learning
Introduction to unsupervised learning - density estimation - restricted Boltzmann machines
Introduction to clustering - clustering algorithms - clustering validation
Guest lecture by Fabian Rost: Dimensionality reduction - practical example of genome data analysis
Classification - short introduction to statistics - naive Bayes classifier
Decision trees and support vector machines
Regression and regularisation
Biological and artificial neural networks
Statistical physics of deep neural networks
Statistical physics of deep neural networks - Convolutional neural networks - Recurrent neural networks - Generative adversarial neural networks
Reinforcement learning
Research seminar by Anna Poetsch (BIOTEC) on using deep neural networks to predict genome instability in cancer.
Lecture notes and video recording not available.