2025-02-18 Advanced Machine Learning with scikit-learn
Advanced Machine Learning
Reiter
About this training
Advanced Machine Learning with scikit-learn
You are already familiar with Machine Learning and scikit-learn but are not sure how to apply it?
Or perhaps you've already successfully applied it to some problems yet not sure your way is optimal and are looking to tools to streamline you studies? Join this course to learn how to efficiently create complete workflows including essential steps such as data normalization, imputation, scikit-learn pipelines, visualisations, etc.
Or perhaps you've already successfully applied it to some problems yet not sure your way is optimal and are looking to tools to streamline you studies? Join this course to learn how to efficiently create complete workflows including essential steps such as data normalization, imputation, scikit-learn pipelines, visualisations, etc.
Course Objectives:
In this course participants will learn the key steps to establish a successfull machine learning pipeline and streamline your investigation.
In particular we will look into following tools:
In particular we will look into following tools:
- Data inspection with non-linear embedding
- Probabililistic gaussian clustering
- K means - cluster number selection and metric selection
- Clustering discrete data
- Data Normalization
- Data imputation
- Pipelines
- Data IO
- Visualization techniques and interactive visualizations
Target group
- Students and Staff of UniBe.
Prerequisites
- Participants must bring own laptops and have a google account - the experiments will be demonstrated using Google Colab.
- You should be familiar with Python and the scientific Python stack, in particular Numpy
- You are familiar with basics of scikit-learn, ideally attended the introductory "Machine learning with Scikit-learn" course.
Methods
Students will perform hands-on exercises using Jupyter notebooks
Certificate
- A certificate will be delivered to participants who have attended the whole training.
Coaches
- Dr. Mykhailo Vladymyrov is a Data scientist and software engineer in bio-imaging at the University of Bern.
- Matteo Boi is a Data Scientist at the University of Bern.
Practical information (time, location ...)
Time : 2025-02-18 09:00-17:00
Location : Room 220, Uni Mittelstrasse, Mittelstrasse 43
Online Participation: https://unibe-ch.zoom.us/j/67605866943?pwd=QXY1Y3YwbjhuWEd5VCtVcWhISXlMdz09
Location : Room 220, Uni Mittelstrasse, Mittelstrasse 43
Online Participation: https://unibe-ch.zoom.us/j/67605866943?pwd=QXY1Y3YwbjhuWEd5VCtVcWhISXlMdz09
Training language: English
Participants : Max 25
Registration : Mandatory
Coaches : Dr. Mykhailo Vladymyrov (lecturer and coach), PD Dr. Sigve Haug (responsible)
Prerequisites : Laptop
Certificate : Certificate for full training attendance
Participants : Max 25
Registration : Mandatory
Coaches : Dr. Mykhailo Vladymyrov (lecturer and coach), PD Dr. Sigve Haug (responsible)
Prerequisites : Laptop
Certificate : Certificate for full training attendance
Material
About DSL
The Data Science Lab is there to boost your research by supporting you solving computing challenges.
https://www.dsl.unibe.ch/
https://www.dsl.unibe.ch/