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Symbol Kurs

483760-HS2023-0: Mathematical Methods for Data Science and Machine Learning

Data Science Fundamentals as introductory course for Continuing Education in Extended Intelligence


Applied Mathematics and Machine Learning

A bridge between the pure theoretical knowledge and practice opens up a new dimension of thinking. On one hand you will see how can you develop new mathematical intuition by putting your theoretical question into the proper practical context. On the other hand you will experience how mathematical skills support the understanding of practical solutions from machine learning.

Machine learning has become a universally applicable beloved tool in the recent years. Parallelly to that an increasing number of people are developing curiosity for it. There are available already many handy packages for machine learning. However to benefit the most of it (to be able to interpret the results and to get out the best from a model) good mathematical understanding of the background machinery can make a big difference.

The aim of this module is to remodel your mathematical knowledge by changing the focus from the abstract mathematics itself towards mathematics with a specific purpose and equipping you for a deeper dive into machine learning.

The material of this course will be organised in Jupyter notebooks and the used programming language will be Python.

Reference books:
  1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning (Adaptive Computation and Machine Learning series), 2016
  2. Andriy Burkov, The Hundred-Page Machine Learning Book, 2019
  3. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent System, 2019
Learning outcomes - participants can/know
  • Linear algebra
    • vector operation and matrix operations,
    • projections,
    • eigenvalues, eigenvectors,
  • Calculus
    • extreme values of functions,
    • vector-valued functions,
    • (directional) gradient of functions,
    • approximation of functions,
    • chain law,
    • backpropagation,
    • gradient descent method.
  • Regression models
    • linear,
    • non-linear,
    • logistical.
  • PCA (principal component analysis)
  • Statistics
    • random variables, mean, variance and covariance,
    • bayes’ theorem.
Target group
  • Students of the CAS Applied Data Science, CAS Natural Language Processing and of the CAS Advanced Machine Learning.
  • Participants must bring own laptops.
  • No programming experience required.
  • Graduate maths knowledge is required.
  • The training alternates between short theoretical introductions and practical exercises.
Time : 2023-08-15 - 2022-08-18, 09:15 - 12:30 (4 half days)
Location : University of Bern, UniTobler, room F - 121 (new room!).
Dr. Kinga Sipos



18. Aug 2023, 09:15 - 12:30: Mathematical Methods for Data Science and Machine Learning

Um dieses Objekt zu nutzen, müssen Sie angemeldet sein und entsprechende Zugriffsrechte besitzen.