Join this course for a practical understanding of Machine Learning and take the first step towards developing your own data-driven model.
If you are a water professional curious about Machine Learning (ML) and have a basic grasp of common Python libraries, this course is designed for you. It will provide a structured overview of the concepts and a guided hands-on experience.
Upon completion of the course, you will be able to:
· Navigate through the ML concepts and types of models
·
Frame, build, test and tune your first ML model
·
Use the core functions of scikit-learn and keras python library
This course includes six hybrid modules consisting
of both offline and online sessions. Each week you will be provided with
pre-recorded material, practical examples and recommended readings. You can
process the offline content at your own pace, as it best fits your work
schedule. Each Wednesday we will meet virtually in an online session where
we’ll go through the key concepts of the week and answer your questions.
At the end of the third module you will
start building you own ML model to solve the course assignment. We will assist
you along the way and provide feedback by the end of the course. In the last
three modules, we will introduce some more advanced ML models to inspire and
guide you on your learning journey. For maximum benefits, you are strongly
advised to allocate three hours of self-study before each live session.
This online course takes place over six weeks, and includes a one-hour live session every week. Participants may choose to join a morning session (8am/9am UTC) or an evening session (1pm/2pm) depending on their schedule.
Wednesday, 25 October 2023
Module 1: Machine Learning mindset and Data Analysis Topics: Definition of Machine Learning (ML) with examples, Overview of ML models, Problem Framing, Exploratory Data Analysis
Wednesday 1 November 2023
Module 2: Classification models Topics: Pre-processing, Model selection, Feature importance, Evaluation, Introduction to scikit-learn library
Wednesday, 8 November 2023
Module 3: Regression models Topics: Pre-processing, Model selection, Loss functions, Bias/variance tradeoff
Wednesday 15 November 2023
Module 4: Neural networks Topics: Neural Network architecture, Activation functions, Gradient descent, Introduction to keras library
Wednesday 22 November 2023
Module 5: Working with time series Topics: Temporal patterns and seasonality, Forecasting approaches
Wednesday 29 November 2023
Module 6: Working with images Topics: Image pre-processing, Strategies for image classification and object detection
Paul is a remote sensing specialist at DHI with a background in physics and mathematics. He has been working at DHI since 2021, focusing on satellite data processing and machine learning.
www.linkedin.com/in/paul-senty-eo
Rocco has a PhD in data-driven modelling of urban drainage systems and has worked as a consultant for climate adaptation projects. His current focus at DHI is on machine learning and its potential applications in urban water management.
www.linkedin.com/in/r-palmitessa
€500 excl. VAT