Fall Term (September - December)
Statistical Modelling and Inference
Deterministic Models and Optimization
Data Warehousing and Business Intelligence
Economic Methods for Data Science
Winter Term (January - March)
Computational Machine Learning
Stochastic Models and Optimization
Pricing Financial Derivatives
Quantitative Methods of Market Regulation
Quantitative and Statistical Methods II
Workshop on Deep Learning
In 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)
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
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.