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2020-08-25 CAS AML M1 Machine Learning Review

CAS Advanced Machine Learning Module 1

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About Module Machine Learning Review
CAS Advanced Machie Learning Module 1
 
See study plan on math.unibe.ch/cas_aml 

Learning outcomes
  • know general concepts and methods of machine learning
  • can design, tune, and train neural networks
  • can measure performance of neural networks Learning objectives
Learning objectives
  • Learning Algorithms
  • Capacity, Over- and Under-Fitting
  • Hyper-Parameters and Validation Set
  • Estimators, Bias and Variance
  • Maximum Likelihood Estimation
  • Bayesian Statistics
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Stochastic Gradient Descent
  • Building a Machine Learning Algorithm
  • Challenges Motivating Deep Learning
Target group
  • Graduates and professionals enrolled for the CAS Advanced Machine Learning 
Prerequisites 
  • University or University of Applied Sciences level degree (bachelor, master, phd), Python or other programming experience, some math, statistics and data analysis experience.
Methods
  • Lectures, tutorials, discussions, project work with presentation.
Practical information (time, location ...)
Time : 2020-08-25 - 28 09:00 - 12:30
Location : Fabrikstrasse 8 , room B005, University of Bern

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias)
Responsible : PD Dr. Sigve Haug
Schedule
Module 1 Machine Learning Review
 
Tuesday 2020-08-25
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30              Lunch
13:30 - 17:00 Individual work with notebooks

Wednesday 2020-08-26
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30              Lunch
13:30 - 17:00 Individual work with notebooks
 
Thursday 2020-08-27
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30              Lunch

13:30 - 17:00 Individual work with notebooks

Friday 2020-08-28
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30              Lunch
13:30 - 17:00 Individual work with notebooks
Datasets
During the CAS you ideally work on your own datasets from work, research or private project. However, the lecturers can also you provide you with links to suitable datasets. 
Readings
You are not supposed to read many books during this CAS, but of course you can. What you need to consult all the time is online documentation on all topics, google brings you there - and wikipedia of course.

To warm up a bit, you may read Tom Wolfe's account on the rise of the Silicon Valley:
https://web.stanford.edu/class/e145/2007_fall/materials/noyce.html

The technical (Python) skills required are covered in the Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook

The CAS content follows more or less the chapters 5 to 20 in the https://www.deeplearningbook.org.
Lecturers and Coaches
Dr. Geraldine Conti (lecturer)

Geraldine has a PhD from EPFL, has been a research associate at Harvard, a CERN fellow, a research associate at Disney Research, associated professor in machine learning and is now head of the machine learning group at PAG.

PD Dr. Sigve Haug (director of studies)

Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is working for the Albert Einstein Center for fundamental Physics and the Mathematical Institute of the University of Bern where is leading the Science IT Support unit.