Reproducible and Interactive Data Analysis and Modelling using Jupyter Notebooks (study period II, 4 ECTS)
The aim of this course is to introduce students to the Jupyter Notebook which is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. Through the notebooks, research results and the underlying analysis can be transparently reproduced as well as shared.
During three days with alternating video lectures and hands-on exercises, the participants will learn to construct well-documented, electronic notebooks that perform advanced data analysis and produce publication ready plots. While the course is based on Python, this is not a prerequisite, and many other programming languages can be used.
For more details see the course website.
Dates: December 3-5, 2018 and January 14-15, 2019
Course teachers: , ,
The deadline for registration is November 16. Please fill in the registration form and return it to Ross Church.
Introduction to deep learning (4.5 ECTS)
Recent development in machine learning have led to a surge of interest in artificial neural networks (ANN). New efficient algorithms and increasingly powerful hardware has made it possible to create very complex and high-performing ANNs. The process of training such complex networks has become known as deep learning and the complex networks are typically called deep neural networks.
The aim of this course is to introduce students to common deep learnings architectues such as multi-layer perceptrons, convolutional neural networks and recurrent models such as the LSTM. Basic concepts in machine learning till also be introduced. The course consists of a series of lectures and computer exercises. The programming environment will be python (Jupyter notebook) together with the deep learning libraries Keras and Tensorflow.
Preliminary Schedule and content:
All lectures take place between 13:15 - 15:00 in seminar room HUB, theoretical physics.
The literature will consist of parts from the deep learning book and lecture notes.
Course responsible: Mattias Ohlsson
Teachers: Mattias Ohlsson + additional teachers and guest lecturers
Distributed computing concepts and tools (study period II, 4.5 ECTS)
The course gives introduction into concepts of geographically distributed computing, such as that implemented by research e-infrastructures dealing with large data volumes and high processing rates. The course is a combination of lectures and hands-on tutorials, addressing aspects of security, authentication and authorisation, interfaces to computing services, principles of distributed storage and data handling, resource characterisation and discovery, information representation and monitoring, workload management and scheduling in a distributed environment. It introduces concepts of services, interfaces, resource virtualisation, meta-protocols, non-interactive workloads, digital certificates and trust, Virtual Organisations, information systems and execution environments. Existing tools and services are introduced as well. The students will obtain personal certificates and a temporary access to a distributed computing infrastructure for exercises. Assessment is done on the basis of the course project, during which the students are expected to make use of distributed computing to solve their daily tasks.
Teachers: Oxana Smirnova, Balazs Konya, Florido Paganelli
Course responsible: Oxana Smirnova
Members of COMPUTE will be notified by e-mail when each course is open for
registration. For further questions please contact the study directors:
List of previous courses here.
This page was last modified on 2 November 2018.