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Practical Machine Learning
Date: 06.06.2019 9:00 - 07.06.2019 16:00
Location details: The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.
Language: english-language
lecturers: Markus Koskela (CSC) Mats Sjöberg (CSC)
  • 120 for-finnish-academics
  • 560 for-others
The fee covers all materials, lunches as well as morning and afternoon coffees.
The seats are filled in the registration order. If a cancellation is received five (5) business days prior to the course, the course fee will be refunded with the exception of a handling fee of 10 €. For no-shows and cancellations after the cut of date no refunds will be made. Registration can be transferred to someone else from the same organization without additional charge.

Payment can be made with electronic invoicing, credit card, or direct bank transfer. Note that for electronic invoicing you need the operator and e-invoicing address (OVT code) of your organization. Please also note that invoice reference is needed for electronic invoicing in your organization, so please have this available when registering.
Additional Information

This course gives a practical introduction to machine learning, including basic approaches to classification, regression, dimensionality reduction and unsupervised learning. We will cover, among other things, linear classification and regression, nearest neighbor methods, support vector machines, decision trees and neural networks.

The course consists of lectures and hands-on exercises using Python with Scikit-Learn and other relevant machine learning libraries. CSC's Notebooks environment will be used in the exercise sessions.

Learning outcome

After the course the participants should have the skills and knowledge needed to begin applying machine learning for different tasks and utilizing the resources available at CSC for training and deploying their own implementations.


The participants are assumed to have a basic knowledge of Python. See also our online Python materials (under "Recommended topics" and "Python programming", check "Python basics" and "Numerical computing with Numpy").


Each session typically consists of a short lecture and a Notebooks exercise.


Day 1, Thursday 6.6.

9:00-10:30 Prologue, basic math, introduction to Notebooks
10:30-10:45  Coffee break
10:45-12:00 Machine learning basics
12:00-13:00 Lunch
13:00-14:15 Classification, linear classifiers
14:15-14:30 Coffee break
14:30-15:15 Nearest neighbor classifiers
15:15-16:00 Regression

Day 2, Friday 7.6.

9:00-9:45 Support vector machines
9:45-10:30 Decision trees
10:30-10:45  Coffee break
10:45-12:00 Neural networks
12:00-13:00 Lunch
13:00-14:30 Dimensionality reduction and visualization
14:30-14:45 Coffee break
14:45-16:00 Unsupervised learning