Introduction to Machine Learning (with Python) ONLINE

Aims

With the rise of new technologies, the volume of data in various fields of research has grown exponentially in recent times and a major issue is to mine useful predictive knowledge from these data. The analysis of such data is not trivial and Machine Learning (ML) is a necessary tool to extract knowledge and make predictions that can be generalized beyond the laboratory.

The practical sessions of this course will be conducted using Python3 and will introduce the widely applied scikit-learn ML framework. The course expects participants to already have a solid grasp of Python and aim to acquire a first understanding of the standard ML methods and processes as well as the practical skills in applying them to real world problems.

Content

During this course we will cover:

• The ML taxonomy and the commonly used machine learning algorithms and terms.
• How to implement common ML algorithms using the scikit-learn Python framework.
• Interpretation and visualization of the results obtained from ML analyses.
• Exploratory Data Analysis and its key role in data analysis.
• Unsupervised Learning: Clustering and Dimensionality Reduction.
• Supervised Learning: Classification and Regression with logistic Regression, Random Forests, and others.
• How the ML toolbox can help us to avoid overfitting and overconfidence as well as to take difficult data handling decisions.

Methods

This course will be delivered through Jupyter notebooks blending presentations of the concepts, code demonstrations as well as exercises and their solutions.
The course materials as well as instruction for the installation of required software and libraries will be provided prior to the course.
As the course will take place online, a computer setup with dual monitors (one to follow the presentation and another to work with the course material) is recommended. But working with one monitor works as well.
The course will mix shorter exercise sessions with longer ones where the participants will work in breakout rooms.
Additionally, some exercises may be completed between course sessions.

Target Group

All Doctoral Candidates & Postdocs

Requirements

Knowledge / competencies
Familiarity with the Python programming language and pandas data frames as well as a basic knowledge on statistics is required. Before applying to this course, please assess your Python skills using the quiz here (we recommend a score of at least ¾ in the basic python section, and 2/3 in the advanced and visualization sections).
No prior knowledge of ML concepts and methods is required.

Technical
You will need to have a recent python3 version as well as a number of python libraries installed. Please follow these instructions to setup your environment (note: these instructions use conda to manage the different packages)
Please perform these installations PRIOR to the course and contact us if you have any trouble.

About the Trainer

Wandrille Duchemin is a Scientific Support specialist and bioinformaticiant at the Center for Scientific Computing (SciCORE) at the university of Basel, and a trainer for the Swiss Institute of Bioinformatics. He holds a PhD in bioinformatics and has five years of experience helping various scientists with the computational and statistical aspects of their research as well as teaching these topics.

Workload

Course attendance: 16h

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.