practical_ml_nov2019 - Training
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Date: | 19.11.2019 9:00 - 20.11.2019 17:00 |
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) |
Price: |
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The fee covers all materials, lunches as well as morning and afternoon coffees. |
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.
Practicalities: event-support@csc.fi
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.
Prerequisities
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 Notebooks exercises.
Day 1, Tuesday 19.11.
9:00-10:30 | Prologue, 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, Wednesday 20.11.
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 |