Data Science Summer School

Course Outline

This is an intensive 20-hour course based on a hands-on approach using Jupyter notebooks, all material is motivated by specific information retrieval and data analysis questions and each thematic unit concludes with a project.

The course provides training in information retrieval, data analysis and machine learning with Python and R and evolves along the thematic following units:

1. Programming with Python
   Keywords: data types, functions, objects
2. Data analysis with Python
   Keywords: pandas, database management, seaborn
3. Scraping data
   Keywords: scraping, API's, cloud storage
4. Supervised learning
   Keywords: sklearn, linear models, cross validation, regularisation, lasso, trees, ensembles, boosting
5. Unsupervised learning
   Keywords: clustering, factors, independent component analysis and matrix factorization
6. Crash course in R
   Keywords: packages, statistical analysis in R, data visualization

About the Instructors

Ioannis Arapakis is a Research Scientist at Telefónica Research, working on Information Retrieval, User Behaviour Modelling and Data Mining. Ioannis received his M.Sc. degree in Information Technology from the Royal Institute of Technology (KTH), Sweden. He then pursued a Ph.D. degree at the SoCS of the University of Glasgow, in the area of Information Retrieval. Between 2011-2015, he worked as a Researcher at Yahoo Labs and was a member of the "User Engagement", "Web Retrieval", and "Ad Processing and Retrieval" groups, where he addressed research problems related to pattern recognition, predictive modelling, multimedia mining and search, social media analysis, and machine learning. He has several publications in top-tier venues and journals, including a SIGIR best paper award, and he is a regular PC member of conference venues such as SIGIR, ECIR, CIKM, and WWW.

Omiros Papaspiliopoulos is ICREA Research Professor at UPF. He is the Scientific Director of the Barcelona GSE Master's Degree in Data Science. His research has appeared in the top journals in Statistics, including several articles in the Journal of the Royal Statistical Society Series B, Biometrika and the Annals of Statistics. He has been an Associate Editor for the first two journals and a Deputy Editor for Biometrika. He has delivered more than 80 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society's Guy Medal in Bronze. His research interests include Monte Carlo Methods, Computational Methods, Bayesian Statistics, Stochastic Processes, Machine Learning.

Nandan Rao is a product of the Barcelona GSE Master's Degree in Data Science (Class of 2017) and is currently pursuing his long-time dream of becoming an economist, working towards a PhD at Universitat Pompeu Fabra. Previously, he was Head of Engineering at machine-learning startup Relink Labs in Copenhagen, taught Fullstack Javascript programming at Codeworks, and put data science in production with research projects at the IOM, the OECD, and The World Bank. Before all of that, he made movies. 

* Laptop required In order to participate, you must bring your own portable computer.

Short Description

The data Social Sciences have access to these days are heterogeneous, unstructured, big in some ways and small in others, and make appropriate use of Machine Learning necessary. However, as opposed to other applications of Machine Learning which focus on prediction, in Social Sciences interpretability, inference and intervention analysis are of high priority. This summer school develops methods from modern Statistics and Machine Learning that are essential to modern Social Sciences and its priorities. The school provides a concise treatment of variable selection, treatment-effect estimation in presence of vast number of controls and what is more generally known as post-selection inference, graphical modelling, Bayesian networks and estimation of graphs from data, causal inference, latent variable and topic models for high-dimensional categorical data, e.g. text or choice data, and elements of Bayesian computation. The summer school follows a hands-on approach using R/Python Jupyter notebooks where theory and implementation are interweaved - most of the examples used are taken from the scientific and consulting work of the course instructors. The school includes project sessions where participants are supervised by data scientists for working on practical cases related to the course.

Requirements

Applicants to all Summer School programs should meet the basic entry requirements. Ideally, applicants would have followed the Foundations of Data Science, in the first week of the Data Science summer school, or be comfortable with the Statistics and Machine Learning material covered there. This is not necessary, but it will ensure that applicants will make the most of the school experience. In any case, applicants are expected to have a basic knowledge of linear algebra, basic computing skills, familiarity with any kind of programming language (not necessarily R, Python), and good understanding of the fundamental concepts in Statistics and/or Econometrics. Previous knowledge of Bayesian statistics is not required. Accepted applicants will be given some material for best preparation for the school.

Course Outline

In the course we develop the following main components: 

1. high-dimensional regression, variable selection and estimation of treatment effects in presence of very large number of instruments. We cover both modern optimization-based penalized likelihood approaches, such as the lasso, but also probabilistic inference approaches such as Bayesian variable selection and model averaging
2. Graphical models and their statistical learning
3. Probabilistic models for high-dimensional data, such as latent topic models
4. Causal inference
5. Advanced computational methods for statistical learning

About the Instructors

Stephen Hansen is Associate Professor in the Department of Economics at the University of Oxford and a Fellow of University College. He was previously Assistant then Associate Professor of Economics at Pompeu Fabra University after receiving his PhD in Economics from the London School of Economics in 2009. He serves as an academic consultant to the Bank of England, a Turing Fellow at the Alan Turing Institute, and is an Associate of the Centre for Economic Policy Research, CESifo, and the Oxford Man Institute. A main line of his research addresses the organization of monetary policymaking institutions, and his recent work has developed the use of machine learning algorithms for text data to answer questions in this area. His academic papers have been published in leading international journals, including the Quarterly Journal of Economics, Review of Economic Studies, Journal of Monetary Economics, and Journal of International Economics.

Omiros Papaspiliopoulos is ICREA Research Professor at UPF. He is the Scientific Director of the Barcelona GSE Master's Degree in Data Science. His research has appeared in the top journals in Statistics, including several articles in the Journal of the Royal Statistical Society Series B, Biometrika and the Annals of Statistics. He has been an Associate Editor for the first two journals and a Deputy Editor for Biometrika. He has delivered more than 80 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society's Guy Medal in Bronze. His research interests include Monte Carlo Methods, Computational Methods, Bayesian Statistics, Stochastic Processes, Machine Learning. 

David Rossell is Ramón y Cajal Fellow at UPF. Previous appointment include the Biostatistics Dept. at MD Anderson Cancer Center (Houston, USA), IRB Barcelona (Spain) as head of Biostatistics & Biostatistics Unit and the Dept. of Statistics at the University of Warwick as Assistant/Associate professor. His work combines methodological research, such as theory, computation and the development of modelling techniques, and a substantial amount of inter-disciplinary work in biomedicine, chemistry and the social sciences. Specific research interests include high-dimensional inference, experimental design, dimensionality reduction and applied statistical modelling, with emphasis on the Bayesian approach. He authored over 35 peer-reviewed publications and has developed a number of R packages implementing statistical methodology. He has taught undergraduate and graduate courses at the University of Warwick, the Oxford-Warwick OxWaSP PhD program for Big Data and at UPF, shorter courses workshops at various universities, and >70 invited talks at international conferences and workshops.

* Laptop required In order to participate, you must bring your own portable computer.

Data Science Summer School Week 1 and 2 participants are expected to have a basic knowledge of linear algebra, basic computing skills, and familiarity with any kind of programming language.

However, Deep Learning and Applications in the Week 3 is taught at a higher level and participants must know fundamentals of programming and data analysis with Python and fundamental concepts in statistical learning, overall the type of material which is covered in Week 1 of the Data Science Summer School (Foundations of Data Science).

In case candidates for this course are not taking also Week 1 course, they must upload a statement of purpose explaining their qualification for the course at the moment of applying. The statement should include any relevant info about academic knowledge and personal skills and interests as well as professional experience in the sector if any. In case of having joined the Data Science Summer School in the past, this should be mentioned in the statement.

Course Outline

In this course we will introduce several aspects of modern machine learning, deep learning and it’s applications:

1. An overall introduction to Deep Learning covering convolutional networks, recurrent neural networks, autoencoders and multilayer deep networks. The course includes a 2-hour tutorial on how to code these types of networks using the popular ‘keras’ library for python.
2. Deep Learning for Recommender systems where we deal with the application of advanced Machine Learning and Deep Learning methods in recommender systems. Here we address Tensor Factorization, Factorization Machines, 2vec type embeddings, Deep Collaborative Filtering techniques such as Autoencoders for Collaborative Filtering, RNN’s for session-based recommendations and convolutional networks for feature extraction. This session includes a hands-on part where these techniques are applied to real recommendation data sets using the keras python library.
3. Natural Language Processing with Deep Learning, this module will focus on several aspects of modern NLP such as language modeling, word and document embeddings, conversational models and visualization and the use of Deep Learning models to perform these tasks also including a hands-on session where several of these tasks are coded with keras.
4. The course will also include two data hackathons where the aim will to use the knowledge acquired in the previous days of the summer school.

About the Instructors

Alexandros Karatzoglou is the Scientific Director at Telefonica Research, his research focuses on Machine Learning. Alexandros received his PhD in Machine Learning from the Vienna University of Technology (TUWIEN). During his PhD he was a frequent visitor to the Statistical Machine Learning group at the ANU/NICTA in Canberra Australia. He has over 50 papers in the field and has won 3 best paper awards at the ACM RecSys and ECMLPKDD conferences. He is also the author of the core machine learning R package kernlab, and enjoys giving lectures on Machine Learning, Recommender Systems and Computational Statistics.

Ilias Leontiadis is currently a Research Associate at Telefonica Research. In the past he was a researcher at University of Cambridge and received his PhD from University College London (UCL). His research interests include mobile systems, pervasive computing, wireless networks, sensor networks, mobile phone privacy and mobility modeling.