Master Meetings

23.11.2021

MSc in Financial Techonology and Computing

Distributed Algorithms
Prof. Fernando Pedone

Course objectives
Distributed computing systems arise in a wide range of modern applications. This course surveys the foundations of many distributed computing systems, namely, the distributed algorithms that lie at their core. The course provides the basis for designing distributed algorithms and formally reasoning about their correctness. It addresses issues related to what distributed systems can and cannot do (i.e., impossibility results) in certain system models.
Course description
The course focuses on three aspects of distributed computing: system models, fundamental problems in distributed computing, and application of distributed algorithms. System models include synchronous versus asynchronous systems, communication models, and failure models. Several fundamental problems are covered, including consensus, atomic broadcast, atomic multicast, atomic commit, and data consistency. Applications of distributed algorithms target various forms of replication techniques.

High-Performance Computing
Prof. Olaf Schenk

Course objectives

Are you interested in using Europe’s faster supercomputers (and getting ECTS credit points for doing so)? Would you like to learn how to write programs for parallel supercomputers, such as a Cray or a cluster of GPUs? The course is designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets.

Course description

The goal of the HPC course is that students will learn principles and practices of basic numerical methods and HPC to enable large-scale scientific simulations. This goal will be achieved within six to eight mini-projects with a focus on HPC, CSE, and AI. The content of the course is tailored for 1st year Master students interested in both learning parallel programming models, scientific mathematical libraries, and having hands-on experience using HPC systems.

Data Analytics for Finance
Prof. Peter Gruber

Course objectives

Tukey (1962) defines Data Analysis to be “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

The goal of this course is to provide the students with the tools and thinking framework to accomplish these tasks with the help of the computer along the entire tool chain of financial data science: from obtaining data to organizing and merging it to analyzing and visualizing it.

Financial Econometrics
Prof. Loriano Mancini

Course objectives
The aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance.

Course description

Building on the material acquired in a basic introductory course in econometrics, the aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance. After a brief review of the classical linear model, three major topics are considered:

• Linear Factor Pricing Model
• Likelihood Methods, with applications to ARCH and GARCH models

Emphasis is placed on the basic understanding of each approach with computer applications on real data.