Zoltán Szabó: recent teaching activity.

2025/26, Winter Term (LSE):

• Graph Data Analytics and Representation Learning (ST457)
• Foundations of Machine Learning (ST510): Kernel Methods. [TBA; 2026]

2025/26, Autumn Term (LSE):

• Machine Learning (ST310)
  • practical sessions: Phil Chan, Seyedpouya Mirrezaeiroudaki, Pingfan Su.

2025, Sept. 26 (LSE):

• Mini-course: Getting Started with Linux

2024/25, Winter Term (LSE):

• Foundations of Machine Learning (ST510): Kernel Methods. [slides; Feb. 10, 2025]
• Graph Data Analytics and Representation Learning (ST457):
  • practical sessions: Shakeel Gavioli-Akilagun.

2024, Sept. 27 (LSE):

• Mini-course: Linux – The Operating System of Freedom [abstract, slides]

2023/24, Winter Term (LSE):

• Foundations of Machine Learning (ST510): Support Vector Machines. [slides; Feb. 5, 2024]
• Graph Data Analytics and Representation Learning (ST457):
  • practical sessions: Shakeel Gavioli-Akilagun.

2023, Sept. 22 (LSE):

• Mini-course: Linux – The Operating System of Freedom [abstract, slides]

2023, May 23 (LSE):

• Mini-course: Linux – The Operating System of Freedom [slides]

2022/23, Lent Term (LSE):

• Graph Data Analytics and Representation Learning (ST457).

2022/23, Michaelmas Term (LSE):

• Artificial Intelligence (ST449):
  • practical sessions: Francesca Panero, Marcos Barreto.

2021/22, Michaelmas Term (LSE):

• Artificial Intelligence (ST449):
  • practical sessions: Marcos Barreto.

2021, Spring (École Polytechnique):

• Mini-course: Independence Measures and Testing [slides; March 24, 2021]

2019, Summer (INRIA):

• Kernel Methods, Divergence and Independence Measures, Hypothesis Testing [July 15-17, 2019]
  • Summer School on Data Science for Document Analysis and Understanding. [slides]

2019, Spring (HEC Paris):

• Data for Management Certificate:
  • with Karim Lounici, Sylvain Le Corff.

2019, Spring (École Polytechnique & Télécom ParisTech):

• Structured Data: Learning, Prediction, Dependency, Testing (MAP667I; M2 MDA):
  • with Florence d'Alché-Buc, Slim Essid.
• Advanced Machine Learning (MAP541, HEC561; Master of Data Science X-HEC):
  • with Stéphane Canu, Erwan Le Pennec, Thomas Kerdreux.
  • tutoring: Jaouad Mourtada, Nicolas Prost.

2018, Autumn (École Polytechnique):

• Statistics (MAP 531; Master of Data Science X-HEC):
  • with Stéphanie Allassonière, Elodie Vernet, Geneviève Robin, Wei Jiang.
• Introduction to Machine Learning (MAP534; Master of Data Science X-HEC):
  • with Julie Josse, Erwan Scornet, Sylvain Le Corff, Florian Bourgey.

2018, Summer (INRIA):

• Kernel Methods, Divergence and Independence Measures, Hypothesis Testing [July 18 & 20, 2018]
  • Summer School on Data Science for Document Analysis and Understanding [slides].

2018, Spring (École Polytechnique & Télécom ParisTech):

• Structured Data: Learning, Prediction, Dependency, Testing:
  • with Florence d'Alché-Buc, Slim Essid, Arthur Tenenhaus, Alexandre Garcia.
• Advanced Machine Learning (MSc Big Data For Business - X/HEC):
  • with Stéphane Canu, Erwan Le Pennec, Anne Auger.

2017, Autumn (Carnegie Mellon University):

• Kernel-based Dependency Measures and Hypothesis Testing [Nov. 27, 2017]:
  • Machine Learning Department @ CMU, Guest Lecture.
  • Slides, course site.

2017, Summer (INRIA):

• Manifold Learning and Classification for EEG Analysis [July 27, 2017].
  • Summer School on Mathematical and Computational Methods for Life Sciences [slides].

2017, Spring (Télécom ParisTech):

• Structured Data: Learning, Prediction, Dependency, Testing:
  • with Florence d'Alché-Buc, Slim Essid, Arthur Tenenhaus.

2016, Autumn (École Polytechnique):

• Functional Data Analysis.

2015 & 2016, Spring (University College London):

• Advanced Topics in Machine Learning - Theory of RKHS:
  • with Arthur Gretton, Kacper Chwialkowski.
  • Slides: PDF; note: PDF.
• Adaptive Modelling, Introduction to Kernel Methods:
  • with Arthur Gretton, Wittawat Jitkrittum, Heiko Strathmann.
  • Theory (slides): part-1, part-2.
  • Practice:
    • Handout: PDF,
    • Files: TextData.mat, naive_bayes.m, naive_bayes_gaussianfit.m, gaussexample.m, mixtureexample.m (from Dino Sejdinovic),
    • SVMlight: info, ZIP.