Workshop "Deep Neural Networks for Beginners" at CIM
Abstract
The workshop "Deep Neural Networks for Beginners" will be hosted by the Centre of Interdisciplinary Mathematics (CIM) in Uppsala during October 14-16, 2019, and feature Prof. Ribana Roscher (University of Osnabrück, Germany, http://rs.ipb.uni-bonn.de/) as teacher. The proposed course builds upon a similar course offered at the GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany, in February 2019, which was very well received.
Motivation: The need for methods to automatically, objectively, and efficiently analyze and interpret data is a common task in many scientific areas. Advances in the field of deep learning have led to a development in machine learning methods such as deep neural networks, which outperform classical methods in many fields. One of the main reasons of their success is the ability to uncover hidden and complex structures in the data, where layered architectures are employed to extract a deep and rich hierarchical feature representation.
Aim: The workshop aims at laying foundations in machine learning and provide necessary deep learning tools in the context of applied sciences. It includes lectures about fundamental and advanced concepts in neural networks and deep learning, which will be presented with allocated time for discussions. The gained knowledge will be applied in three hands-on sessions covering various practical aspects. The sessions will cover all necessary aspects of machine learning pipelines that work on real world applications, covering data pre-processing, model learning and testing, as well as quantitative and qualitative evaluation.
Workshop objectives:
Understanding the basics in classification and regression
Understanding the basics in neural networks
Application of neural networks in hands-on sessions (Python programming sessions)
Usage of Keras and Tensorflow to train and build deep learning models for applied sciences
Analysis and evaluation of results obtained in hands-one sessions
Participation in interactive discussion
Presentation of results
A course evaluation will be carried out during the week following the course to collect the feedback
Covered topics:
Basics in machine learning: classification + regression (learning, testing, evaluation)
Challenges in machine learning
Classification paradigms and classification tasks
Representation learning
Basics in neural networks and deep learning
Backpropagation
Cross-validation
Convolutional neural networks, recurrent neural networks, Long-short-term memory networks
