Teaching

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
2023	Róbert Gers	High performance extraction of financial market data
2023	Andreas Piper Mårtensson	Investigation and implementation of Machine Learning Pipelines within the area of image analysis and classification
2023	Tamas Janos Paulik	Implementation and orchestration of a scalable and automated Machine Learning Operation pipeline
2023	Stefanos Rodopoulos	Exploring Bio relationships through Counterfactual Optimisation Analysis using VAE
2023	Joshua Sebastian	Using Deep Learning and MLOps to detect Dementia through Speech Analysis
2023	Alejandro Rodriguez Salamanca	Memory Efficient Methods for Large Models
2023	Anna Bzinkowska	Machine Learning approach to product categorization in the manufacturing industry
2023	Melina Siskou	Optimizing Machine Learning Operations in Logistics: Exploring Best Practices and Evaluating Tools
2023	Milad Taghikhani	Exploring Multimodal Data Integration to Large Language Models and LLM-based generative models

Former students:

Year	Students	Project
2023	Thomas Spyrou	Monitoring black-box classification models in machine learning systems
2023	Laurine M Dargaud	Developing a spontaneous speech-based machine learning model for the early detection of dementia
2023	Spyridon Vlachospyros	Context-aware object detection using deep learning
2023	Jonah Jad Tabbal	AutoML and Meta-learning through data science competitions
2023	Mads Dudzik Møller	MLOps in a Deep Learning enabled production environment
2023	Joachim Andersson and Jonas Hoffmann	Exploring the Extensions and Limitations of Metadata Archaeology via Probe Dynamics
2023	Julius Radzikowski and Carl Schmidt	Using meta-labeled data to improve deep-learning classification model robustness, and boost data quality
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	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	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