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 handson 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
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).
Date  Topic  Slides  Other 

Feb 27  predictive models, least squares regression, model selection, regularization  slides (PDF)  
Mar 1  real data, nonvectorial data, other regularizers  slides (PDF)  
Mar 6  missing data, nonlinear regression  slides (PDF)  
Mar 8  robust regression, classification  slides (PDF)  
Mar 13  model evaluation, largescale 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  Kmeans clustering  
May 15  Independent component analysis, project Q&A  
May 17  Project presentations 


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