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2020-06-12 + 2020-06-15 DSF5 Machine Learning with Python

Introduction to basic machine learning with Python.

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About this course - learning outcomes
DSF 5 Machine Learning with Python

Basic introduction on how to perform typical machine learning tasks with Python.

 
Learning outcomes
  • Day 1:
    • Overview of machine learning pipelines and their implementation with scikit-learn
    • Regression and Classification: linear models and logistic regression
    • Decision trees & random forest models
    • Principal component analysis (PCA) and non-linear embeddings (t-SNE and UMAP)
    Day 2:
    • Clustering with K-means and Gaussian mixtures
    • Artificial Neural networks as general fitters, fully connected nets used to classify the fashion-MNIST dataset
    • Scikit-learn and clustering maps, Q&A
Target group
  • Students, researchers and professionals working with data
Prerequisites
  • Equivalent to Data Science Fundamentals 1, 2 and 3.
  • Basic programming skills are necessary, we don't reserve time for basic programming concepts.  
Methods
  • Course languages are Python and English
  • Short theory sessions followed by hands-on tutorials with Jupyter notebooks 
Certificate and points
  • Certificate of attendance.
  • Participants attending  DSF 4-5 are offered to make a project (30 hours). Upon a successful presentation 2 ECTS credit points are given. 
Coaches
  • The coaches are local and external experts
Practical information (time, registration, location ...)
Time : 2020-06-12 + 2020-06-15 from 09:00 to 17:00 
Location : Room 124, Mittelstrasse 43, University of Bern and online

Training language: English
Participants : Max 24
Registration : Mandatory (use the Join button. Note that it is necessary to be alreay logged into Ilias, button in the upper right corner)
Coaches : PD Dr. Sigve Haug (responsible)
Fee: Free of charge
Certificate: Certificate of Attendance and part of Data Science Fundamentals (2+2 ETCS)
Schedule and content
  •  Friday 12 June
Morning:
09:00 - 09:10: Welcome
09:10 - 10:30: General intro in ML, datasets, skl interface
10:30 - 11:00: Break
11:00 - 12:30: Linear models & logistic regression
12:30 - 13:30: Lunch

Afternoon:
13:30 - 15:00: Trees & Forests
15:00 - 15:30: Break
15:30 - 17:00: PCA & Embeddings
  • Monday 15 June
Morning:
09:00 - 10:30: Clustering
10:30 - 11:00: Break
11:00 - 12:30: Intro in NN & image fit
12:30 - 13:30: Lunch

Afternoon:
13:30 - 15:00: Fully connected net for F-MNIST classification
15:00 - 15:30: Break
15:30 - 16:50: scikit-learn map / clusterings map, Q&A
16:50 - 17:00: Thanks & final remarks
About ScITS
PD Dr. Sigve Haug

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. 

The Science IT Support is there to boost your research by supporting you solving computing challenges. 
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - drop an email, call or pass by our office. Cheers, Sigve

Our wepgabe is scits.unibe.ch.