Training eSupport System
  • Log In
    • Log in with LS Login
    • Login
    • Register
  • Spaces
  • Events
  • Materials
  • Workflows
  • Collections
  • e-Learning
  • Learning paths
  • Directory
    • Providers
    • Nodes

TeSSHub makes use of some necessary cookies to provide its core functionality. Additionally, we make use of Google Analytics to discover how people are using TeSSHub in order to help us improve the service. To opt out of this, choose the "Allow necessary cookies" option.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies Allow all cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month
    • Scientific topic
    • Statistics
    • Active learning10
    • Ensembl learning10
    • Kernel methods10
    • Knowledge representation10
    • Machine learning10
    • Neural networks10
    • Recommender system10
    • Reinforcement learning10
    • Supervised learning10
    • Unsupervised learning10
    • Bayesian methods4
    • Biostatistics4
    • Descriptive statistics4
    • Gaussian processes4
    • Inferential statistics4
    • Markov processes4
    • Multivariate statistics4
    • Probabilistic graphical model4
    • Probability4
    • Statistics and probability4
    • Network1
    • Pathway1
    • Pathway or network1
    • Show N_FILTERS more
    • Content provider
    • Glittr.org3
    • GTN1
    • Show N_FILTERS more
    • Keyword
    • Machine learning
    • Statistics and machine learning44
    • ai-ml9
    • elixir9
    • jupyter-notebook8
    • Statistics6
    • Large Language Model5
    • interactive-tools4
    • work-in-progress3
    • Deep Learning2
    • Image Learner2
    • Python2
    • deep-learning2
    • jupyter-lab2
    • machine-learning2
    • Artificial intelligence1
    • Digital Humanities1
    • GLEAM1
    • GTEx1
    • Gene Expression1
    • HAM10000 Dataset1
    • HANCOCK Dataset1
    • Image Classification1
    • LORIS Score Model1
    • Machine Learning1
    • Multimodal Learning1
    • Pan-cancer1
    • Pathways and Networks1
    • Phylogenetics / Phylogenomics1
    • Recurrence Prediction1
    • Skin Lesion Classification1
    • Tabular Learner1
    • Tissue Classification1
    • cancer biomarkers1
    • dephosphorylation-site-prediction1
    • fine-tuning1
    • image-segmentation1
    • oncogenes and tumor suppressor genes1
    • protein-3D-structure1
    • text mining1
    • Show N_FILTERS more
    • Competency level
    • Not specified3
    • Beginner1
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International
    • License Not Specified9
    • Apache License 2.02
    • Creative Commons Attribution Share Alike 4.0 International1
    • Show N_FILTERS more
    • Target audience
    • Students1
    • Show N_FILTERS more
    • Author
    • SIB Swiss Institute of Bioinformatics2
    • Daniel Blankenberg1
    • Neuromatch Academy1
    • Vijay1
    • Show N_FILTERS more
    • Contributor
    • Wandrille D.2
    • Aalok Varma1
    • Alish Dipani1
    • Ann Kennedy1
    • Anup Kumar1
    • Athena Akrami1
    • Bernard Marius 't Hart1
    • Byron Galbraith1
    • Bérénice Batut1
    • CodeWizard1
    • Domenic1
    • Ella Batty1
    • Eric1
    • Ethan Cheng1
    • Federico d'Oleire Uquillas1
    • Furkan Ozcelik1
    • Gunnar Blohm1
    • Helena Rasche1
    • Himanshu Aggarwal1
    • Iryna Yavorska1
    • James Arney1
    • Jeffrey Erlich1
    • Jesse Livezey1
    • Jesse Parent1
    • Jorge A Menendez1
    • Kshitij Dwivedi1
    • Lin Zhong1
    • Madineh Sarvestani1
    • Marcel Stimberg1
    • Marcelo G Mattar1
    • Marco Brigham1
    • Marina1
    • Marius Pachitariu1
    • Matt McCann1
    • Matthew Krause1
    • Max Myroshnychenko1
    • Michael Waskom1
    • Nima Dehghani1
    • Patricia Palagi1
    • Patrick Mineault1
    • Pierre-Etienne Fiquet1
    • Rafael Grigoryan1
    • Richard Gao1
    • Ritobrata Ghosh1
    • Saad Jbabdi1
    • Saeed Salehi1
    • SanjeevNara-011
    • Saskia Hiltemann1
    • Scott Linderman1
    • SebastienBoyer1
    • Siddharth Suresh1
    • Sophie Laturnus1
    • Spiros Chavlis1
    • Steeve Laquitaine1
    • Tara van Viegen1
    • Titipat Achakulvisut1
    • Vasudev Sharma1
    • Vijay1
    • Viviana Greco1
    • Xaq1
    • Yaroslav Halchenko1
    • Yoni Friedman1
    • Zoltan1
    • actions-user1
    • carsen-stringer1
    • courtneydean331
    • idupanloup1
    • kbonnen1
    • masaomi1
    • mayofaulkner1
    • rellum-sukram1
    • texnh1
    • vincentvalton1
    • Show N_FILTERS more
    • Resource type
    • e-learning1
    • Show N_FILTERS more
    • Related resource
    • Associated Training Datasets1
    • Associated Workflows1
    • Show N_FILTERS more
    • Node
    • Switzerland3
    • Show N_FILTERS more
  • Show materials from all spaces
  • Show disabled materials
  • Show materials with broken links
  • Show archived materials

Training materials

  • Subscribe via email
  • Harvest using OAI-PMH

Email Subscription

Harvest using OAI-PMH

Exchange content using OAI-PMH

Use an OAI-PMH compatible tool to harvest metadata using the OAI-PMH endpoint. In particular, this endpoint can be used for exchanging content between different TeSS instances. See the TeSS documentation for more details.

Register training material

Keywords: Machine learning

and Licence: Creative Commons Attribution 4.0 International

and Scientific topics: Statistics

4 materials found
  • NeuromatchAcademy/course-content

    ELIXIR node event
    Pathway or network Machine learning Statistics and probability Statistics Machine learning Python Pathways and Networks Artificial intelligence
  • sib-swiss/statistics-and-machine-learning-training

    ELIXIR node event
    Machine learning Statistics and probability Statistics Machine learning
  • sib-swiss/intro-machine-learning-training

    ELIXIR node event
    Machine learning Statistics and probability Machine learning Statistics
  • e-learning

    PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis

    • Beginner
    Statistics and probability Machine learning Pan-cancer Statistics and machine learning cancer biomarkers oncogenes and tumor suppressor genes
Training eSupport System
[email protected]
Contribute
About TeSSHub
Browse Spaces
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
Source code
API documentation
Bioschemas testing tool

TeSSHub has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.