Main reference for different Machine Learning algorithms :

• Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  Christopher M. Bishop (2006): Pattern Recognition and Machine Learning.
  Lutz, Mark. Learning python. " O'Reilly Media, Inc.", 2013.

• MPI :mpi4py.readthedocs.io/en/stable/
• Hadoop : https://hadoop.apache.org/docs/stable/
• TensorFlow : https://www.tensorflow.org/api_docs
• PyTorch : https://docs.pytorch.org/docs/stable/index.html

This implementation-oriented course offers hands-on experience with current algorithms and approaches in Machine Learning and Artificial Intelligence, and their application to real-world learning and decision-making tasks. Praktikum will also cover empirical methods for comparing learning algorithms, for understanding and explaining their differences, for analyzing the conditions in which a method is more suitable than others.
On weekly basis, we shall implement varying machine learning algorithms in a distributed more efficent manner; this includes using Message Passing Inteface (MPI), Hadoop distributed file system, and different distributed computing techniques in both tensorflow and PyTorch.
The programming language for this course will be Python. We will also look at most popular libraries for solving different models.

MSc WI & IMIT & AINF & DA -  MSc 1-3