#### Computational modelling: methods and applications (2021-2022)

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.

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

#### Foundations of machine learning I, II (2020-2021) and III (2021-2022)

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

#### MATE5349 Artificial intelligence for schoolteachers (2021-2022)

This course is a general introduction to artificial intelligence, designed for the students on the teacher tracks in mathematical sciences. We focus on clarifying what artificial intelligence is, on what can be done with artificial intelligence, and on various societal concerns around the wide adoption of artificial intelligence. The course aims to provide an exploration of the nature of the problems addressed in artificial intelligence and of the computational strategies behind the most popular approaches in this field. The topics we cover will include design and analysis of machine learning experiments, training a model and using it for predictions. We will discuss clustering, classification and deep learning. Short assignments include hands-on experiments with various learning algorithms. We will also have an active discussion on ethical aspects of AI and on ideas of how short lectures on AI could be offered in school classrooms.

After passing this course, the students will be able to describe the basic algorithmic workflow of artificial intelligence solutions from data to models to learning and predictions. They will be able to describe how machine learning models works for clustering, classification, and deep learning. They will be able to give short introductory lectures on artificial intelligence in the classroom. They will also be able to discuss briefly some of the main societal concerns around artificial intelligence.

#### Introduction to automata theory (2021-2022)

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.

#### 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.

#### 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

Computability is the area of computer science that investigates computation as its main subject of interest: what is computation, how can a computation be carried out, why some problems are impossible to solve by computers, why some problems are difficult to solve, what degrees of difficulty are there. This field that was virtually inexistent some …

#### Computational processes in living cells

Viewed as information processing systems, biological organisms possess amazing capabilities to perform information-handling tasks such as: control, pattern recognition, adaptability, information-storage, etc. Thus, the functioning of biological organisms as information-processing systems is of great interest to computer scientists, and we are witnessing now a fast growing research in this field. …

#### 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 …