To get you on your way with Machine Learning, the course lecturers have set up a document to offer a “first step” to learn about the different methods, without necessarily having to follow the course: GP-OML Machine Learning – basics
When you are still interested in participating in the course, you can pre-register for Spring 2023: see below.
Because of the great interest for this course, and the limited number of places available: TRAIL/Beta/OML/ERIM members have first choice. And also: first come, first go.
The objective of this course is to provide knowledge in machine learning models and techniques. Both fundamentals as well as practical applications are discussed.
After a successful completion of this course, students will be able to:
– Implement different machine learning models.
– Understand how data can be used to provide new insights into problems.
– Compare different techniques and algorithms in advanced machine learning,
– Understand how to choose a model to describe a particular type of data or problem.
– Evaluate machine learning models in practice.
– Understand the mathematics necessary for constructing novel machine learning solutions.
– Design and implement various machine learning algorithms in a range of real-world applications.
This course introduces the fundamental methods of machine learning and statistical pattern recognition. This course will cover both theoretical foundations as well as implementation of machine learning in the data mining context. We will analyze data to create predictive and prescriptive models with (un)supervised machine learning methods, such as regression, clustering, tree based methods ensemble methods, (deep) neural networks, and Gaussian processes.
This course will introduce the end-to-end process of investigating data through machine learning methodology. The goal is either to discover / generate some preliminary insights in an area where there really was little knowledge beforehand, or to be able to predict future observations accurately. This includes methods to extract and identify useful features that best represent your data.
The sessions will focus on theoretical aspects of machine learning methods and algorithms, but also on hands-on experience using a suitable programming language. The course will not focus on particular applications. That is the objective of the optional project, where you can use the foundations provided for an application from your own scientific area. Examples of machine learning applications in the operations management and logistics field are provided during the sessions.
= preliminary =
Day 1: Introduction to machine learning concepts. Tree based methods and ensembles. Assessing model accuracy and model interpretability. (Instructor: Rui Jorge Almeida, Maastricht University)
Day 2: Gaussian processes regression. Introduction to Bayesian optimization. (Instructor: Inneke Van Nieuwenhuyse, Hasselt University)
Day 3: Neural networks. Feedforward neural networks, hidden layers, back-propagation, bagging and regularization. (Instructor: Rui Jorge Almeida, Maastricht University)
Day 4: Unsupervised learning. Clustering, association rule learning, dimension reduction. (Instructor: Rui Jorge Almeida, Maastricht University)
The material for this course includes selected papers and chapters from:
– Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. New York, NY,USA: Springer New York Inc.. ISBN 978-0-387-84858-7.
– Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An introduction to statisticallearning : with applications in R. New York :Springer. ISBN: 978-1461471370.
– Goodfellow, I., Bengio, Y. Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0-262-035613.www.deeplearningbook.org.
– Murphy, K.P., (2012), Machine Learning A Probabilistic Perspective. MIT Press. ISBN: 978-0-262-01802-9
– Han, J., Pei, J., Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. ISBN: 978-1-55860-901-3
– Rasmussen, C.E., Williams, CK.I. (2005). Gaussian Processes for Machine Learning (AdaptiveComputation and Machine Learning). The MIT Press. www.gaussianprocess.org/gpml/
– Forrester, A., Sobester, A., & Keane, A. (2008). Engineering design via surrogate modelling: a practicalguide. John Wiley & Sons.
– Gramacy, R. B. (2020). Surrogates: Gaussian Process Modeling, Design, and Optimization for theApplied Sciences. CRC Press.
A selected list of papers will be made available throughout the course, by email.