Data Science Events Calendar

Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.


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Machine Learning, Predictive Modeling, and Validation – for Battery State-of-Health Estimation (2024)

May 21 - May 22

Organiser: Associate Professor Daniel-Ioan Stroe,

Assistant Prof. Søren B. Vilsen (AAU-MATH)
Associate Professor Daniel-Ioan Stroe (AAU-Energy) 

ECTS: 2.0

Date: 21 – 22 May 2024

Deadline: 30 April 2024

Place: AAU Energy, Pontoppidanstraede 101 room 1.015, Aalborg, Denmark

Format: in person

Max no. of participants: 30

This two-day course introduces key aspects of machine learning, predictive modelling, and model validation. Focusing on quantitative predictive models for Lithium-ion battery state-of-health modelling. The course will present an end-to-end framework from when data is gathered to a model has been created and used for state-of-health estimation.


Day 1: Lithium-ion batteries and ML-based feature extraction and reduction by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours

– Introduction to lithium-ion batteries and battery performance parameters for SOH
– Overview of machine learning methods, the bias-variance trade-off, and cross-validation.
– Feature extraction (manual extraction).
– Feature reduction through principal components analysis and multi-dimensional scaling.

Day 2: Machine Learning for battery SOH estimation by Daniel-Ioan Stroe and Søren B. Vilsen; 7.5 hours

– Linear models, selection, and shrinkage methods.
– Kernel methods: support vector regression and Gaussian process regression.
– Neural networks: DNN and RNN.
– Automatic feature extraction and reduction by using neural networks.

Prerequisites: Fundamental understanding of probability and statistics is recommended. Furthermore, basic knowledge of either R, Matlab, or python is strongly recommended.

Form of evaluation: Students are expected to solve several exercises and deliver an individual report with solutions and comments.

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