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Foundations of machine learning I and II (2020-2021)

This course (part I and II) is a general introduction to machine learning focusing on the fundamental modern topics in this field and providing the theoretical bases and concepts behind key algorithms. The course aims to provide a deep understanding of the nature of the problems addressed in machine learning and of the computational strategies behind the most popular approaches in this field. The topics we cover include: design and analysis of machine learning experiments, supervised learning, unsupervised learning, active learning, reinforcement learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, kernel machines, graphical models. Short programming assignments include hands-on experiments with various learning algorithms.

Coursebook:

  • Ethem Alpaydin. Introduction to machine learning, MIT Press, 2020.

Other materials:

  • Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020.
  • Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, Second Edition, 2018.
  • Avrim Blum, John Hopcroft, and Ravindran Kannan. Foundations of Data Science. Cambridge University Press, 2020.

Registration to the courses: through this link.

To the course webpage: Foundations of machine learning I, Foundations of machine learning II

Introduction to automata theory (2020-2021)

This course gives a basic introduction to the theory of finite automata. The course got first through the principles of mathematical induction, Boolean algebras and matrices. It then introduces (deterministic and nondeterministic) finite automata and regular expressions, and it establishes the connections between them. The course is meant for Bachelor students in mathematics and computer science.

To the course webpage: TBA

Foundations of machine learning (2019-2020)

This course focuses on the mathematical foundations of basic machine learning concepts and algorithms. Using mathematical language we aim to express widely used machine learning concepts that seem intuitively obvious, but turn out to be surprisingly difficult to use optimally in practice. The aim is to gain insights into several basic machine learning tasks, to understand what they do, what they are best at, and what their limitations are.

The course is an excellent introduction to machine learning for mathematics students. It is also highly suitable to computer science students as a companion to the machine learning engineering courses, providing the mathematical background to the algorithmics and the programming methods they introduce.

Coursebook:

  • Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020.

Other materials:

  • Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, Second Edition, 2018.
  • Avrim Blum, John Hopcroft, and Ravindran Kannan. Foundations of Data Science. Cambridge University Press, 2020.

To the course webpage

Introduction to computational and systems biology (2019-2020)

This course aims to give an introduction to the broad field of computational and systems biology. The course takes a mathematical / computer science perspective on the field, focusing on the algorithmic and computational challenges and opportunities in this area.

To the course webpage

Computational modelling: methods and applications (2019-2020)

This course aims to provide an introduction to the entire computational modelling process, from the formulation of a qualitative model, to its quantitative formulation, to model fitting and validation, model analysis, and model predictions. We focus on the various computational methods that can be employed for modelling and especially on the advantages (and disadvantages) of each approach.

This is an online, MOOC-type of a course. Students follow the video lectures online at their own pace, discuss with the lecturer and the other students through the course forum, solve exercises and small projects. Several in-campus sessions will be organised as well to support the learning.

To the course webpage

An earlier version of the course is freely available through YouTube.

Advanced computational modeling

This course builds onto the introductory Computational modeling techniques course. It aims to provide a deeper view into several computational modeling techniques. We discuss in the course modeling with stochastic processes, with Petri nets and with rule-based systems. We …

Computability and computational complexity

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Computational processes in living cells

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Introduction to Biocomputing

Research at the border of Molecular Biology and Information Technology has witnessed in recent years an exciting development, with remarkable benefits for both areas. On one hand, biological data is …