EdUp Twin Project: Machine Learning: from Mathematical Foundation to Implementation in PYTHON

Machine Learning: from Mathematical Foundation to Implementation in PYTHON 

These lectures are part of the collaborative teaching project EdUp (TUBAFdigital). 

Table of Content: 

Prof. Dr. Dmytro Babets, Department of Applied Mathematics, Dnipro University of Technology

1. Linear Algebra in Machine Learning
2. Clustering
3. Principal Component Analysis
4. Introduction to Neural Networks
5. Data Preparation and Machine Learning Pipeline

Prof. Dr. Björn Sprungk, Institut für Stochastik (Fakultät 1), TU Bergakademie Freiberg

1. Introduction to Machine Learning
2. Probability Theory for Machine Learning
3. Linear and Logistic Regression
4. Support Vector Machines and Kernels
5. k-Nearest Neighbour and Division Trees

“Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Erasmus+ National Agency for Higher Education (German Academic Exchange Service). Neither the European Union nor the granting authority can be held responsible for them.”