Teaching
Machine Learning Operations
I am the main lecture on course 02476 Machine Learning Operations at Technical University of Denmark, having developed most of the material myself.
Webpage: https://skaftenicki.github.io/dtu_mlops
DTU course page: https://kurser.dtu.dk/course/02476
The course introduces students to a number of coding practices that will help them organization, scale, monitor and deploy machine learning models either in a research or production setting. To provide hands-on experience with a number of frameworks, both local and in the cloud, for doing large scale machine learning models.
Supervision
Feel free to reach out to me if you have an interesting project proposal and is missing a supervisor.
Current students:
| Year | Students | Project |
|---|---|---|
| 2022 | Simon Moe Sørensen | Deep multimodel modelling of images and text from wine |
| 2022 | Victor Girardin Flindt | Fault detection in industrial production processes using Deep learning methods |
| 2022 | Jakub Wladyslaw Szreder | Creating a Machine learning pipeline for training and evaluation of models for medical data |
| 2022 | Nicolai Weisbjerg and Kelvin Foster | Quantifying Uncertainty in Semantic Segmentation using Bayesian Deep Metric Learning |
| 2022 | Frederik Kjær and Jonas Christian Rask Levin | Construction of a Recommendation system for best next buy recommendations |
| 2022 | Bekarys Gabdrakhimov | End-to-end machine learning project on classification of EEG signal of depressed patients |
| 2022 | Magnus Falkenberg | Advanced Machine Learning Operation for GAN networks |
Former students:
| Year | Students | Project |
|---|---|---|
| 2022 | Xianhao Liu | Continues piecewise affine based normalizing flows |
| 2022 | Andri Geir Arnarson and Asger Frederik Græsholt | Automation of a fruit sorting system |
| 2022 | Gustav Selfort Hartz | Using deep learning and transformer models for processing, searching, and tagging legal documents |
| 2022 | Marco Placenti | Machine Learning Pipeline Engineering with Amazon Web Services |
| 2021 | Frederik Kromann Hansen and Jonas Søbro Christophersen | Investigating osteoarthritis via x-ray images using deep learning |
| 2021 | Morten Holm Thomsen and Simon Kristian Jacobsen | Development of Graph Neural Networks for prediction of financial data |
| 2020 | Kathrine Thorup Hagedorn | Entertainment Recommender System complying to the GDPR ruleset |
| 2020 | Dominik Mate Kovacs and Zoltán Kovács | Generating and Detecting Deepfakes |
| 2018 | Julie Liv Cetti Hansen | Foci detection in cells using deep learning |