Jülich Supercomputing Centre

# PRACE Tutorial Parallel and Scalable Machine Learning

February 2020

## Abstract

The course starts by teaching the basics of machine learning and data mining algorithms to understand the foundations of ''learning from data''. Then the course points to key challenges in analyzing large quantities of data sets in order to motivate the use of parallel and scalable machine learning algorithms.

## Lecture 1 – Parallel and Scalable Machine Learning driven by HPC

## Lecture 2 – Introduction to Machine Learning Fundamentals

## Lecture 3 – Supervised Learning with a Simple Learning Model

## Lecture 4 – Artificial Neural Networks (ANNs)

## Lecture 5 – Introduction to Statistical Learning Theory

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## Lecture 6 – Validation and Regularization

## Lecture 7 – Pattern Recognition Systems

## Lecture 8 – Parallel and Distributed Training of ANN

## Lecture 9 – Supervised Learning with Deep Learning

## Lecture 10 – Unsupervised Learning – Clustering

## Lecture 11 – Clustering with HPC

## Lecture 12 – Introduction to Deep Reinforcement Learning

## Practicals (codes and Jupyter notebooks)

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