Introduction to Deep Learning

The workshop will take place on July 29th, 10:00 AM CEST on Zoom

Course Objectives

  1. Understand what Deep Learning is.
  2. Apply Deep Learning algorithms.
  3. Think about how you could apply Deep Learning in your own work.

Course Content

Neural networks—long believed dead—are back. Advances in training deep neural network ar- chitectures have had a tremendous impact on Computer Science and Machine Learning for a decade. Today, they are the foundation of many modern data science applications in academia and industry.

This class will provide an introduction into deep neural networks. Starting from flat architectures, we will understand how neural networks use backward propagation of prediction errors to op- timize their performance. Adding more layers to the initial set-up, we will then go on and build increasingly “deep” architectures. We implement fully connected deep learning architectures and learn about classical pitfalls and how to tackle them. If time permits, we will also give up the full connectedness between nodes and take a first look at models that aggregate information to more abstract data representations and recurrent data.

For teaching, the class will alternate between lecture style blocks and more hands-on workshop phases where participants implement code themselves. A range of different languages exist to build deep learning models. This class relies on R and KERAS as main APIs, since they might offer the most intuitive entry point for Social Scientists. You do not have to prepare your local machines, since we will deploy all code on RStudio Cloud.

For the class, it is certainly helpful if you are confident in using R and if you are familiar with the maths behind a logistic regression. It is also of great help if you take the Machine Learning workshop a day before.

Instructor

Christian Arnold
Prof. Christian Arnold

Prof. Christian Arnold is a Senior Lecturer at Cardiff University. Using data driven methods from statistics and machine learning, his work lies at the intersection between social science and computer science. Recently, he has been working more and more with neural networks to tackle challenges in data privacy, NLP and applied computer vision. His substantive research focuses on institutions in governance. What drives and determines the rules of political decision making? Prior to joining Cardiff, he had a position at Oxford University and worked as a Data Scientist in industry. He graduated with a PhD in Political Science from the University of Mannheim. He is a member of the academic advisory board for the Government Statistical Service at the Office for National Statistics. His focus there is on all matters related synthetic data.


Schedule

Session Starts

Introduction to Deep Learning (Part I)

Short Break

Session Continues

Introduction to Deep Learning (Part II)

Session Ends


Content Licensing

All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.


Workshop Registration