My main teaching revolves around Artificial Intelligence and Machine Learning in different formats and for different audiences. I was awarded a final round nomination for the “Best Teaching - Best Practices” award of my university in 2017 for my contribution under the topic “Wissenschaftlichkeit” (“scientific character”), and won it (3rd place) in 2019 for the “Vermittlung von Grundlagen” (“teaching of foundational matters”). My shorter teaching statement from 2008 is still valid: “I love teaching. To initiate inside someone the vision of an idea, to see understanding grow, these are moments I greatly enjoy.”
TOC
- Online courses (direct link to video lectures: AI 2018 & ML 2019)
- Supervised thesis projects (all levels)
- Lectures & curriculae
Online courses
I try to make my main courses available as free online ressources as a best effort offer.
- Artificial Intelligence (KI1): final year elective course in the Bachelor of Computer Science degree program of ZHAW. Available online with full course material, videos (currently shot: HS 2018) and audio-only recordings. Taught in German, material in English.
- Machine Learning (TSM_MachLe): elective technical scientific specialization module on the Master of Science in Engineering program, focus area ICT / Data Science. Material & lectures in English.
Supervised thesis projects (all levels)
| When | Level | Type | Title |
|---|---|---|---|
| Fall 2024 | PhD | Thesis | Strategies for Practical Deep Learning |
| Fall 2024 | BSc | PA | AI-based interpretation of engineering drawings |
| Fall 2024 | BSc | PA | Enhancing SwissGPT with Agentic RAG |
| Spring 2024 | MSc | VT1 | Overcoming Sample Scarcity and Label Ambiguity in Cell Segmentation and Classification of Thyroid Cancer: A Kernel-Based Approach on Top of CellSAM |
| Spring 2024 | BSc | BA | Prosodic Feature Modelling in Transformers for Speaker Verification follow-up research internship Students: Fabian Bosshard, Andrin Fassbind |
| Spring 2024 | BSc | BA | A2C2 - Natural Language-Instructed Autonomous Agent for Computer Control |
| Spring 2024 | MAS | Thesis | Beaker detection using realtime image analysis and artificial neural networks |
| Fall 2023 | PhD | Thesis | Deep Learning for Robust and Explainable Models in Computer Vision |
| Fall 2023 | MSc | MT | Predicting DARPin Protein Binding using Geometric Graph Neural Networks |
| Spring 2023 | MSc | MT | Self-Organisation in a Biologically Inspired Learning Framework Based on Bernoulli Neurons top grade, paper Student: Pascal Sager |
| Spring 2023 | BSc | BA | RoboDog III: Building a Vision and /or Sound-Based AI Demonstrator on a Robotic Platform top grade Students: Juri Pfammatter, Daniel Schweizer |
| Spring 2023 | BSc | BA | Deep-Learning-based Cell Segmentation for the Detection of Thyroid Cancer in Single Cells top grade, Regional Siemens Excellence Award, Lab Sciences Award 2024, National Siemens Excellence Award, Publikumspreis, ZHAW SGD Award, 3rd. prize, paper Students: Tenzin Samdrup Langdun, Martin Oswald |
| Spring 2023 | BSc | BA | Teaching Neural Nets to Learn Dynamic Voice Features for Automatic Speaker Verification |
| Spring 2023 | MAS | Thesis | Lean Production Monitoring (with Cameras) |
| Fall 2022 | MSc | VT2 | Supporting DARPin binder selection through deep learning |
| Fall 2022 | MSc | VT1 | Reproducing a large-scale Speaker Verification System |
| Fall 2022 | MSc | VT1 | The Practical Impact of Data-Centrism on the Example of Autonomous Driving |
| Fall 2022 | BSc | PA | RoboDog II: Building a Vision and /or Sound-Based AI Demonstrator on a Robotic Platform top grade Students: Tenzin Samdrup Langdun, Martin Oswald |
| Fall 2022 | BSc | PA | Speak your mind! Brain Computer Interfaces for Communication |
| Fall 2022 | BSc | PA | Machine-Learning-based Analysis of Data from the ZHAW Movement Analysis Laboratory for Fatigue Detection during Sports Exercises |
| Spring 2022 | MSc | MT | Leveraging Neuroscience for Deep Learning Based Object Recognition |
| Spring 2022 | MSc | VT1 | Automatic extraction of anthropometric features and body composition parameters from computer tomography images enables improved BMI prediction at scale |
| Spring 2022 | BSc | BA | Making AI Tangible: Building a Computer Vision Demonstrator on a Robotic Platform |
| Spring 2022 | BSc | BA | Learning to Learn: Learning Successful Weight Patterns for Instant Deep Neural Network Training top grade Students: Urban Lutz and Alexandre Manai |
| Fall 2022 | MSc | MT | Entropy-Aware Active Vision through Voxelized Octree Exploration of 3D Scenes |
| Fall 2021 | MAS | Thesis | Similarity Analysis of Jazz Tunes with Vector Space Models |
| Fall 2021 | MAS | Thesis | Confidence-Rated Predictions with Deep Learning for Music Object Detection |
| Fall 2021 | MSc | VT2 | End-to-End Pipeline for Body Composition Analysis and Sarcopenia Detection without Target Labels top grade, paper Student: Pascal Sager |
| Fall 2021 | BSc | PA | Universal Songbook: Real Time Chord Prediction from Live Audio |
| Fall 2021 | BSc | PA | Forschungs-PA: Continual Deep Learning for Visual Recognition |
| Spring 2021 | MSc | VT1 | Exploiting Temporal Information in Speech using Deep Learning |
| Spring 2021 | MSc | VT2 | Multimedia Retrieval using Vision Transformers |
| Spring 2021 | MSc | MT @ HSLU | Deep Learning with Noisy Image Labels in Real-World Computer Vision Applications _paper at SDS2021> Student: Niclas Simmler |
| Spring 2021 | BSc | BA | Neural Network-based Image Synthesis to Improve Automatic Segmentation of Medical Images for COVID paper at CISP-BMEI 2021 Students: Jonathan Gruss and Yves D. Stebler |
| Spring 2021 | BSc | BA | Transfer Learning of Deep Neural Network Representations of Brain Activity top grade Students: Benjamin Bertalan and Gian Andri Hess |
| Fall 2020 | PhD | Thesis @ Ca’Foscari / Venice | external examiner for “Using Contextual Information In Weakly Supervised Learning” |
| Fall 2020 | BSc | PA | From human to artificial neural networks: Deep-learning based analysis of brain activity |
| Spring 2020 | MSc | MT | Exploiting the Full Information of Varying-Length Utterances for DNN-Based Speaker Verification |
| Spring 2020 | BSc | BA | Active Scene Understanding from Image Sequences for Next-Generation Computer Vision top grade, paper at AVHRC 2020, and Dr. Waldemar Jucker award 2020 Students: Dano Roost and Ralph Meier |
| Spring 2020 | MAS | Thesis | Neural-network-based Speaker Diarization on Federal Assembly Sessions |
| Spring 2020 | MSc | MT @ EPFL | Multi-Agent Reinforcement Learning for Common Pool Resource Appropriation top grade Student: Zeki Doruk Erden |
| Spring 2020 | PhD | Thesis | Exploiting Contextual Information with Deep Neural Networks successfully defended Candidate: Ismail Elezi |
| Fall 2019 | MSc | MT | Medical Image Analysis using Deep Learning |
| Fall 2019 | MSc | VT2 | WaveVoice: A Wavenet Based Architecture for Speaker Verification |
| Fall 2019 | MSc | VT1 | Real-World Speaker Recognition on VoxCeleb2 using Angular Margin Losses top grade Student: Claude Lehmann |
| Fall 2019 | BSc | PA | Reinforcement Learning mit einem Multi-Agenten System für die Planung von Zügen paper at SDS 2020 Students: Dano Roost and Ralph Meier |
| Spring 2019 | MSc | VT2 | Baselines for the Flatland Reinforcement Learning for Train Scheduling Challenge |
| Spring 2019 | MSc | VT1 | Additive Angular Margin Loss for Speaker Clustering |
| Spring 2019 | BSc | BA | Benchmarking of Classical and Deep Learning Speaker Clustering Approaches |
| Spring 2019 | BSc | BA | Speaker Clustering for Real-World Data using Deep Learning |
| Fall 2018 | MSc | VT1 | Deep Learning-based Classification of Musculoskeletal Radiographs - Learn to Zoom from Network Response |
| Fall 2018 | BSc | PA | Speaker Recognition & Diarization on Realistic Data |
| Fall 2018 | BSc | PA | Reinforcement Learning Challenge: Compete in Automatic Game Playing for e.g. Doom or Bomberman |
| Fall 2018 | BSc | PA | Machine Learning to Support Artists |
| Spring 2018 | BSc | BA | Recognizing birds by voice - the BirdCLEF 2018 challenge |
| Spring 2018 | BSc | BA | Speaker Diarization of Media Archives with Deep Neural Networks |
| Spring 2018 | BSc | BA | PinballWizard II: Using Reinforcement Learning to Control a Pinball Automaton top grade Students: Joram Liebeskind and Silvan Wehner |
| Spring 2018 | BSc | BA | Understanding Deep Neural Networks |
| Fall 2017 | BSc | PA | Are final grades predictable from resume photos? Debugging deep neural networks based on a controversial claim |
| Fall 2017 | BSc | PA | Deep Learning Verfahren das Malen beibringen: Optimiertes Training für GANs |
| Fall 2017 | BSc | PA | Deep Learning for Speaker Clustering public code repository Students: Jan Sonderegger and Patrick Walter |
| Fall 2017 | BSc | PA | Deep Learning-basierte Voice Conversion mittels problemspezifischer Loss-Funktion |
| Fall 2017 | MSc | MT | Learning to Cluster top grade, paper at ANNPR 2018, and Dr. Waldemar Jucker award 2018 Student: Benjamin Bruno Meier |
| Fall 2017 | MSc | MT | Reinforcement Learning to Play ‘Doom’ |
| Spring 2017 | MSc | VT2 | Deep Learning for End-to-End Voice Conversion - a Survey |
| Spring 2017 | MSc | VT1 | Evaluating OMR Systems |
| Spring 2017 | BSc | BA | Machine Learning for Speaker Clustering paper at ANNPR 2018 Students: Patrick Gerber and Sebastian Glinksi Haefeli |
| Spring 2017 | BSc | BA | Speaker Clustering with Metric Embeddings |
| Fall 2016 | BSc | PA | Recurrent Neural Networks for Speaker Recognition top grade Student: Patrick Gerber |
| Fall 2016 | BSc | PA | Pinball Wizard: Using Deep Reinforcement Learning to Train a Pinball Wizard |
| Fall 2016 | BSc | PA | Deep OMR: New Ways for Optical Music Recognition |
| Fall 2016 | MSc | VT1 | Fully Convolutional Neural Networks for newspaper Article Segmentation top grade and paper at ICDAR 2017 Student: Benjamin Bruno Meier |
| Spring 2016 | MSc | VT1 | Reinforcement Learning for Building Control |
| Spring 2016 | BSc | BA | Instrumentenerkennung in Single-Source Audio Streams top grade Students: Yannick Streit & Leotrim Zulfio |
| Spring 2016 | BSc | BA | Automatische Stimmerkennung mit Deep Learning (Neuronalen Netzen) top grade and paper at MLSP 2017 Students: Yanick Xavier Lukic and Carlo Vogt |
| Spring 2016 | BSc | BA | Künstliche Intelligenz für das Zahlenpuzzle 2048 top grade Student: Micha Schwendener |
| Fall 2015 | BSc | PA | Künstliche Intelligenz für ‘Super Mario’ durch Neuroevolution |
| Fall 2015 | BSc | PA | Sprechererkennung mit Deep Neural Networks top grade and paper at MLSP 2016 Students: Yanick Xavier Lukic and Carlo Vogt |
| Fall 2015 | BSc | PA | Automatische Erkennung der Akkordfolge in Popmusik |
| Spring 2015 | BSc | BA | Erkennung von Hintergrundmusik in Broadcast Audio |
| Spring 2015 | BSc | BA | Entwicklung einer Android-App zur Erkennung von Redeanteilen im Unterricht |
| Spring 2015 | BSc | BA | Deep Learning für automatische Stimmerkennung top grade Student: Gabriel Eyyi |
| Fall 2014 | BSc | PA | Hilti Big Data Competition: Recommender Systeme |
| Spring 2014 | BSc | BA | Talkalyzer: Neue Algorithmen für automatische Sprecher-Erkennung top grade Student: Jan Stampfli |
| Spring 2014 | BSc | BA | Talkalyzer: Mobile-App zur automatischen Sprecher-Erkennung |
| Fall 2013 | BSc | PA | Web-Scale Datenanalyse auf einer Big Data Appliance |
| Fall 2013 | BSc | PA | Einsatz von Graphenalgorithmen zur Verbesserung der Pünktlichkeit von Zügen |
Types:
- PA: Bachelor project thesis
- BA: Bachelor thesis
- VT1: Master specialization project thesis 1
- VT2: Master specialization project thesis 2
- MT: Master thesis
- MAS: Master thesis in continuing education
Lectures & curriculae
Active
| Tag | Name | Format | Type | Content | Did what? | When? |
|---|---|---|---|---|---|---|
| AI1 | Artificial Intelligence 1 | lecture & lab | final year BSc computer science elective course; won best teaching award (3rd place) in 2019 | foundational AI course based on Russell&Norvig’s book | responsible, initiate, create, teach | Spring 2017, fall 2017-2023 |
| AI2 | Artificial Intelligence 2 | lecture & lab | finale year BSc computer science / data science elective course | developing fair algorithms; towards more general neural models | responsible, co-initiate, co-create, co-teach | Fall 2017, spring 2023-2025 |
| CVDL | Computer Vision with Deep Learning | lecture & lab | finale year BSc computer science / data science elective course | modern computer vision based on deep learning methodology (beyond CNNs and classification) | responsible, initiate | Fall 2022 |
| ML | Machine Learning | lecture & lab | continuing education module in CAS Data Science Applications and CAS Machine Intelligence | practical machine learning for data scientists | responsible, initiate, create, teach | Fall 2015-2016, spring 2017-2018, spring 2021, spring 2023 |
| INTAI | Introduction to AI for Engineers | block week | elective final year block course for all engineering programmes | short introduction based on AI1 | responsible, initiate, create, teach | Fall 2024 |
Previously
| Tag | Name | Format | Type | Content | Did what? | When? |
|---|---|---|---|---|---|---|
| RL | Reinforcement Learning | lecture & lab | finale year BSc computer science / data science elective course | introduction to reinforcement learning | initiate | Fall 2022 |
| MLOps | Machine Learning Operations | lecture & lab | finale year BSc computer science / data science elective course | introduction to building, operating and maintaining machine learing pipelines and systems | initiate | Fall 2022 |
| CAS MLOps | Certificate of Advanced Studies in Machine Learning Operations | on-premise one-year continuing education programme | thorough introduction to MLOps and societal aspects of applied AI | initiate | Fall 2022 | |
| MachLe | Machine Learning | lecture & lab | MSc computer science & engineering elective course; won best teaching final round nomination in 2017 | mid-level course on practical machine learning with a focus on deep learning | responsible, initiate, co-create, co-teach | Spring 2017-2020 |
| MSE profile DS | Profile Data Science in the Swiss Master of Science in Engineering | MSc programme | new profile within the joint Swiss engineering master’s degree, consisting of theoretical, foundational, and context modules on analytics, data engineering, and data management | co-initiate, co-create, member of module groups | Spring 2016 | |
| EVA MI Lab | Machine Intelligence Lab | MOOC, colloquia & hackathon | MSc extended advanced module (EVA) | experience an online course on an advanced machine learning topic | responsible, initiate, co-create, teach | Fall 2015-2019 |
| EVA AI Seminar | Artificial Intelligence Semniar | seminar, blog posts, talk | MSc extended advanced module (EVA) | read, understand and write scientific literature | initiate, create, teach | Spring 2017-2019 |
| CAS Machine Intelligence | Certificate of Advanced Studies in Machine Intelligence | on-premise one-year continuing education programme | thorough introduction to applied machine and deep learning, text analytics and big data | initiate, co-create, module responsible | Spring 2017 | |
| DPD | Data Product Design | lecture, lab, guest appearances, video pitch | continuing education module in CAS Data Science Applications and CAS Data Product Design | business basics for techie data scientists | initiate, co-create, co-teach | Fall 2015-2017 |
| PROG2 | Programmieren 2 | lecture & lab | BSc computer science foundational module | advanced Java programming & tooling | co-create, teach | Spring 2014-2017 |
| Scripting | Scripting | lecture & lab | continuing education module in CAS Information Engineering | foundations of scripting for analytics with Python for data scientists | initiate, create, teach | Fall 2014-2015 |
| MAS Data Science | Master of Advanced Studies in Data Science | on-premise multi-year programme | continuing education program | technically oriented data science education for university alumnis | co-initiate, co-create | Spring 2014-2015 |
| IE1 | Information Engineering 1 | lecture & lab | last year BSc computer science elective course | introduction to information retrieval | co-teach | Fall 2015 |
| Big Data | Big Data | lecture & lab | continuing education module in CAS Information Engineering | foundations and tools of processing at scale for data scientists | co-teach | Spring 2015 |
| DSSY | Decision Support Systems | lecture & lab | last year BSc industrial engineering course | data warehousing & big data | co-create, (co-)teach | Spring 2013-2015 |
| ITPROG | IT Programming | lecture & lab | BSc industrial/aviation/transportation engineering foundational module | introduction to programming with Python | initiate, create, teach | Fall 2014-2015 |
| PPRG | Prozedurale Programmierung | lecture & lab | BSc aviation/industrial engineering foundational module | Java for beginners | teach | Fall 2013 |
| PSIT 1&2 | Projektschiene 1&2 | guided project, reviews | BSc computer science foundational module | project track software development | co-create | Fall 2013 |
| DAB1 | Datenbanken 1 | lecture | BSc computer science foundational course | relational algebra, databases, and SQL | guest appearance | Fall 2013 |
| IRG | Information Retrieval Grundlagen | lecture & lab | final year BSc computer science elective course | foundations of textual information retrieval | guest appearance | Spring 2013 |
See also my Marburgian page for several lectures, labs and supervised theses in the area of Multimedia Analysis and Retrieval (2005-2009).