Data Science Workshops

Barcelona GSE Data Science Workshops are sessions conducted by experts on a specific topic. The content and format are designed to be interesting for professionals and academics alike. Workshops last 4-6 hours and are held at Ciutadella Campus (UPF). 

Special pricing is available for professors, PhD students, and Barcelona GSE Alumni. Current students in the Barcelona GSE Data Science Master program attend the workshops for free. Registration is required.

Recent Barcelona GSE Data Science Workshops included:

(alphabetical by title)

Workshop on Conversational Artificial Intelligence
Carlos Segura (Telefónica research)

This course will introduce students to the concepts, techniques and challenges involved in the design and development of conversational agents. Following a brief overview of the dialogue system development, the course will analyse the most recent research for building intelligent conversational agents. Existing commercial bot development platforms and open source alternatives will be introduced and compared. Finally, the last part of the course will be dedicated to hands-on development of a chatbot using the reviewed tools and techniques.

About the speaker

Carlos Segura is a researcher at Telefónica Research.

Workshop on Deep Learning
Alexandros Karatzoglou (Telefónica Research)

The workshops aim at introducing students to the basic concepts of Deep Learning. The workshop consist of 3 hours of introduction to the basic concepts of Neural Networks and the basic Deep Learning architectures along with 3 hours of hands-on programming of Deep Learning models using python and the keras package.

  • Basic Concepts in Deep Learning
  • Stochastic Gradient Descent and variants
  • Embedding methods
  • Convolutional Neural Networks
  • Recurrent Neural Networks

About the speaker

Alexandros Karatzoglou is the Scientific Director at Telefonica Research.

Workshop on Distributed Machine Learning
Ioannis Arapakis (Telefónica Research)

In this workshop we will examine the basic concepts (RDDs, transformations, runtime architecture) behind distributed Machine Learning (ML) using Spark, a fast and general-purpose cluster computing platform. We will motivate the use of distributed ML and introduce MLlib, a library of machine learning functions. MLlib has been designed to run in parallel on clusters, contains a variety of learning algorithms and is accessible from all of Spark’s programming languages (e.g., Java, Python).

During the lab session we will try to implement some basic supervised/unsupervised algorithms introduced in the lecture. We will aim to be using python and some additional useful libraries, such as NumPy and Pandas.

About the speaker

Ioannis Arapakis is a Research Scientist at Telefónica Research in Barcelona.

Workshop on Internet of Things Analytics
Gaston Besanson (Accenture Analytics Innovation Center)

Internet of Things (IoT) is the next ground for Analytics. IoT is spread across the length and breadth of the industry. From consumer electronics, automobiles, aviation, energy, oil and gas, manufacturing, banking, and so on, almost every industry is benefiting from IoT. Data can come from different devices or sensors, data can have different characteristics, diverse latencies, different importance or be plagued with missing values or be exposed through vulnerabilities in the pipeline security.

We, data scientists, are used to developing our solutions on cloud computing architectures, where a server takes care of the entire computation. However, with the IoT ecosystem, this is not always efficient neither cost-effective so we need to rethink our paradigms. Here is where the “edge computing” (the devices/sensor layer) presents itself as a solution. Edge computing is an architecture where the process of data, applications, and services are pushed away from the centralized cloud to the logical extremes of the network. These devices are equipped with the enough computing power and data storage facilities to fulfill the task. After computing, only the rich and condensed yet reusable data is transmitted back to the cloud. Low cost alternative storage can also be considered for the remaining data. The objective of the course is to provide you with a hands-on experience on IoT Analytics that will leverage your already obtained knowledge throughout the Master and equip you with basic skills to deploy your first IoT Analytics solutions.

About the speaker

Gaston Besanson is a Data Scientist at Accenture Analytics Innovation Center in Barcelona and an alumnus of the Barcelona GSE Master's in Data Science.

Workshop on Recommender Systems
Alexandros Karatzoglou (Telefónica Research)

The workshop aim to introduce students to the main methods used to create recommendations based on past user actions. The main idea behind recommender systems is to try and model user preferences using data that the user generated while interacting with a system. The workshop is divided in two parts the first part will be devoted to the introduction to the main machine learning methods used in recommender systems and the second part will be devoted in using these methods in class using python to build a recommender system.

  • Introduction to the main concepts in Recommender Systems
  • Memory-based Collaborative Filtering
  • Model-based Collaborative Filtering
  • Context-aware Recommender Systems
  • Deep Learning for Recommender Systems

About the speaker

Alexandros Karatzoglou is the Scientific Director at Telefonica Research.

Workshop on Reinforcement Learning
Gergely Neu (DTIC, Universitat Pompeu Fabra)

Reinforcement learning (RL) is the model-based theory of sequential decision-making under uncertainty. The field of reinforcement learning research looks back to a long history with several periods of high and moderate success; recent years saw an unprecedented increase in attention to the field both in the mainstream media and within the machine learning/AI research community. Indeed, RL techniques underly many of the breakthrough successes of AI research that made headlines in recent years (e.g., the ATARI and Go players developed at Google DeepMind). Within the machine learning community, several successful and influential researchers now cite the task of general reinforcement learning as the ultimate challenge for all of machine learning research. Very recently, RL has been highlighted as one of the "Top 10 Breakthrough Technologies of 2017" by the MIT Technology Review.

This short course gives an overview of the fundamental techniques of reinforcement learning starting from the classic temporal-difference methods through approximate dynamic programming all the way to recent developments in deep reinforcement learning. The goal is to provide a strong understanding of the most common methods and provide a basic algorithmic toolbox for building RL systems. Besides familiarizing students with these tools, the course puts a strong emphasis on highlighting the crucial challenges that set RL problems apart from other machine learning problems. Students taking the course are expected to gain the capability to identify and tackle such challenges in various application domains.

About the speaker

Gergely Neu is a postdoctoral researcher at Universitat Pompeu Fabra. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Technical University of Budapest, where his advisors were Andras Gyorgy, Csaba Szepesvari and Laszlo Gyorfi. His main research interests are in machine learning theory, including reinforcement learning and online learning with limited feedback and/or very large action sets.

Workshop on Transportation in Machine Learning
Anestis Papanikolaou (JACOBS Urban Mobility, London)

The main objective of this course is to provide a thorough grounding on the key principles of transportation planning and modelling. The theoretical underpinnings of transport science have evolved from many disciplines, most notably economics, statistics, psychology and operational research. Transportation planning and modeling is currently in a transformative phase, as the emergence of new data sources as well as the use of machine learning techniques open up a new spectrum of opportunities for transportation researchers and practitioners.

The course includes a complete introduction to transportation theory (passenger transport) in order to equip students with the fundamental concepts, assumptions, models and algorithms met in transport science and practice. Students will also have the opportunity to apply these methodologies in the context of real world problems met in transportation practice. The final objective is to familiarize students with the appropriate domain knowledge in order to be able to pursue a research and/or professional position in the evolving transportation sector by conveying the data scientist mindset.

About the speaker

Anestis Papanikolaou is Principal Transport Modeller for JACOBS Urban Mobility in London.