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Data Science and Scientific Computing track core course

Instructors:  Bernd Bickel, Christoph Lampert, Gasper Tkacik

Teaching Assistants: Bor Kavcic, Katharina Ölsböck


The course will consist of 3 segments of approximately 4 weeks each. Each segment will follow the same structure: the first two weeks will consist mainly of lectures and background material study, the second two weeks will introduce a small project based on real data or a computational assignment, whose results will then be presented in the last week. The emphasis will be on dealing with data and computations in a hands-on fashion. 

Tentative summary of the course segments:

Segment 1: Predictive models (Instructor: Christoph Lampert)

Goal: understand and be able to handle predictive models

Segment 2: Working with real data (Instructor: Gasper Tkacik)

Goal: Characterizing basic statistical properties of an unknown dataset

Segment 3: Simulations and numerics (Instructor: Bernd Bickel)

Goal: Understand and apply basic computational techniques for simulations in a variety of applied science problems, with focus on differential equations



Requirements/Exams: homework sheets, group projects

Credits: 6 ECTS

Final Grade: 50% homework, 50% projects

Prerequisites: list of skills (PDF)   mock exam to test if you have the prerequisites (PDF)


Segment 2 Lecture Notes, Homework and Project (updated May 15, 2017)

Segment 2 trace1 Data (zipped text file).

Segment 2 trace2 Data (zipped text file).

Segment 2 Ruderman images Data (zipped text files).

Fourier Transform chapters from Numerical recipes (here, here, and here).

Segment 3

Schedule (subject to change)

Date Topic Slides Other
Feb 27 predictive models, least squares regression, model selection, regularization slides (PDF)  
Mar 1 real data, non-vectorial data, other regularizers slides (PDF)  
Mar 6 missing data, nonlinear regression slides (PDF)  
Mar 8 robust regression, classification slides (PDF)  
Mar 13 model evaluation, large-scale model learning slides (PDF)  
Mar 15 project Q&A    
Mar 20 project Q&A    
Mar 22 project presentations    
Apr 3 Introduction: Why do we build models?    
Apr 5 Descriptive statistics, estimating probability distributions    
Apr 24 Estimating error bars    
Apr 26 Comparing distributions, measuring correlations    
May 3 Covariance matrices and Principal Component Analysis (PCA)    
May 8 Basic spectral estimation using FFT and linear filtering    
May 10 K-means clustering    
May 15 Independent component analysis, project Q&A    
May 17 Project presentations    


FileDue Dateextra files
exercises1.pdf Mar 6 2017 diabetes.txt
exercises2.pdfMar 13 2017
project.pdf Mar 22 2017

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