Module 11: Machine Learning with Python
Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teachers - Course material - Fees - Enrol
Type of course
This is an online course.
Dates
Seven Monday evenings in April, May and June 2021: April 19 and 26, May 3, 10, 17 and 31, June 7, 2021, from 5.30 pm to 9 pm.
Please note: the deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is March 19, 2021.
Venue
Online
Description
Many modern digital applications increasingly rely on machine learning as a means to derive predictive strength from high-dimensional data sets. Compared to traditional statistics, the absence of a focus on scientific hypotheses, and the need for easily leveraging detailed signals in the data require a different set of models, tools, and analytical reflexes.
This course aims to bring participants to the level where they can independently tackle the analytical part of data mining projects. This means that the most common types of projects will be addressed - regression-type with continuous outcomes, classification with categorical outcomes, and clustering. For each of these, the practical use of a set of standard methods will be shown, like Random Forests, Gradient Boosting Machines, Support Vector Machines, k-Nearest-Neighbors, K-means,... Furthermore, throughout the course, concepts will be highlighted that are of concern in every statistical learning applications, like the curse of dimensionality, model capacity, overfitting and regularization, and practical strategies will be offered to deal with them, introducing techniques such as the Lasso and ridge regression, cross-validation, bagging and boosting. Instructions will also be given on a selection of specific techniques that are often of interest, such as modern visualization of high-dimensional data, model calibration, outlier detection using isolation forests, explanation of black-box models,... Finally, the last lecture will introduce the idea of deep learning as a powerful tool for data analysis, discussing when and how to practically use it, and when to shy away from it.
Target audience
This course targets professionals and investigators from all areas that are involved in predictive modeling based on large and/or high-dimensional databases.
Exam
Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued. The exam will consist in completing an individual take-home project.
Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.
Incorporation in DTP and reimbursement from DS for UGent PhD students
As a UGent PhD student, to be able to incorporate this 'specialist course' in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is March 19, 2021.
Course prerequisites
Participants are expected to be familiar with basic statistical modeling (as for instance taught in Module 2 of this program), and to have a had a first experience programming in Python (as for instance taught in Module 3 of this program).
Teacher
As a Senior Data Scientist at the KBC Group Big Data center, dr. Bart Van Rompaye heads a group of data scientists applying modern data analytical approaches to investment-related problems. He obtained his PhD at Ghent University on issues in survival analysis, and held postdoctoral positions at Ghent University and Umea University, Sweden. In the past, he has taught numerous courses for the Master in Statistical Data Analysis, the Institute for Continuing Education in Science, and FLAMES, the Flanders Training Network for Methodology and Statistics.
Course material
Copies of the slides and Python code notebooks.
Fees
A different price applies, depending on your main type of employment.
| Employment | Module 11 | Exam |
|---|---|---|
| Industry/Private sector1 | 1.320 | 30 |
| Non-profit, government, university outside AUGent2 | 545 | 30 |
| (Doctoral)student outside AUGent2 | 385 | 30 |
1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.
2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special prices.