Fall Term (September - December)

Statistical Modelling and Inference

Deterministic Models and Optimization

Data Warehousing and Business Intelligence

Computing Lab

Economic Methods for Data Science

Winter Term (January - March)

Machine Learning

Computational Machine Learning

Financial Econometrics

Electives

Stochastic Models and Optimization

Data Visualization

Pricing Financial Derivatives

Quantitative Methods of Market Regulation

Quantitative and Statistical Methods II

Workshop on Deep Learning
I​​n this seminar we are going to present the main ideas and concepts behind Deep Learning. We will motivate the use of deep learning methods in machine learning and introduce three deep network architectures that are extensively used in practice namely: feedforward networks, convolutional networks and recurrent networks.

Workshop on Behavioral Economics
Behavioral economics is concerned with the development of psychologically founded models of decision-making. The relationship between behavioral economics and data-science is bidirectional. Data science allows the testing of behavioral economics models and the interpretation of (big) data relies crucially on counting with sound models of behavior. In this workshop we will introduce the main constructs in the decision-making under risk, inter-temporal choice, and social preferences.

Workshop on Graphical Models: Structures and Algorithms
This seminar will cover basic notions of graphical modes, such as conditional independence, directed acyclic graphs, moralization and Markov blankets, it will review fundamental statistical models constructed as graphical modes, such as hierachical models/Bayesian networks, hidden Markov models, Gaussian graphical models and Markov random fields, and explain algorithms for efficient computations on graphs, such forward-backward algorithms, Gibbs sampling and belief propagation.

Spring Term (April - June)

Electives

Industrial Practicum

Topics in Big Data Analytics I

Topics in Big Data Analytics II

Social and Economic Networks

Text Mining for Social Sciences

Machine Learning for Finance

Digital Market Design

Policy Lessons **

Workshop on Distributed Machine Learning
I​​n 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.