About this course
Artificial Neural Networks are programs that write themselves when given an objective, some training data, and abundant computing power. Recently, these programs have brought about a wide array of impressive innovations, such as self-driving cars, face recognition, and human-like speech generators. This course offers you an introduction to Deep Artificial Neural Networks (i.e. “Deep Learning”). With focus on both theory and practice, we cover models for various applications, how they are trained and tested, and how they can be deployed in real-world applications
Syllabus
Pre-requisites
One year of computer science at university level. One of the computer science courses should be in algorithms and data structures. One course in either probability theory, linear algebra, or statistics at university level. Knowledge of programming languages (e.g. in Python/Javascript/Java/C++/Matlab).
Faculty
Lorenzo Belgrano
FacultyM.Sc. in Mathematical Modelling and Computation, Technical University of Denmark (2019). B.Sc. in Information Engineering, University of Padua (2016). Senior Machine Learning Engineer at Corti (2019–2024). Previous experience implementing deployment pipelines using Docker and Azure ML and developed a FastAPI to request detection to ML models using Tensorflow Serving. With DIS since 2023.
Mossa Merhi Reimert
FacultyPh.D., Veterinary Epidemiology, University of Copenhagen, 2025. Teaching Assistant, University of Copenhagen, 2011-2018. Data Scientist, SHFT ApS, 2015-2016. Student Developer, Inference Labs, 2016-2017. Lecturer/Researcher, Hogeschool van Amsterdam, 2018-2019. Research Assistant, University of Copenhagen, 2021-2022. PhD Fellow, University of Copenhagen, 2019-present. With DIS since 2025.
Iraklis Moutidis
FacultyPh.D. In Computer Science (Natural Language Processing and Social Network Analysis, University of Exeter 2023). Freelance Data Scientist 2021 – Current, Software Developer Moduleering CAE Thessaloniki 2017, Summer Student at CERN Geneva 2016, Research Assistant at CERTH ITI Thessaloniki 2014-2015. With DIS since 2025.
Nicolai Frost Kolborg Jacobsen
FacultyHead of Data Science at Corti (2019–present), leading a team to build a system that leverages Automatic Speech Recognition output to measure call quality. M.Sc. Dual Degree in Data Science, Technical University of Denmark & Korea Advanced Institute of Science and Technology (2017 and 2018, respectively). B.Sc. in Natural Science and IT, University of Copenhagen (2015).. Previous experience building forecasting models as Data Scientist at Scales (2018) and optimized equipment distribution with Python and integer programming as Business Intelligence Consultant at IQVIA (2015–2018). With DIS since 2022.
Lucian Leahu
FacultyPh.D. in Computer Science, Cornell University (2012). Previous experience as Assistant Professor at ITU Copenhagen (2015-2018), ERCIM Postdoctoral Fellow at the Swedish Institute of Computer Science (2012-2013),an and Project Leader in the Media Technology and Interaction Design Department at the Royal Institute of Technology (2014). With DIS since 2019.