Teaching

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

  1. Online courses (direct link to video lectures: AI 2018 & ML 2019)
  2. Supervised thesis projects (all levels)
  3. Lectures & curriculae

Classroom

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 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
Students: Dano Roost and Ralph Meier
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 and paper at ANNPR 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 MSc 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 ICPR 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

Lectures & curriculae

Tag Name Format Type Content Did what? When?
KI1 Künstliche Intelligenz 1 lecture & lab final year BSc computer science elective course; won best teaching award (3rd place) in 2019 practical AI course based on Russell&Norvig’s book responsible, initiate, create, teach Spring 2017, fall 2017-2020
KI2 Künstliche Intelligenz 2 lecture & lab finale year BSc computer science elective course AI applications with a focus on neural networks responsible, co-initiate, co-create Fall 2017
TSM_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
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
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
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
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
PROG2 Programmieren 2 lecture & lab BSc computer science foundational module advanced Java programming & tooling co-create, teach Spring 2014-2017
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).

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