This course describes the use of neural network models in learning and optimization e.g., pattern recognition, routing, and prediction. This course is divided into two parts. The first part (3 weeks) provides an introduction to neural networks, focusing on the so-called Hopfield model, its statistical mechanics and optimization algorithms. The second part (4 weeks) provides a more detailed introduction to learning, describing models, algorithms, and applications. Part 1: An introduction to neural networks: neuroscience, statistics and optimization Neuroscience An example of a neural network: the Hopfield model Statistics and Optimisation (Monte-Carlo methods, simulated annealing) Part 2: Learning: models, algorithms, and applications Supervised learning: simple perceptrons and layered networks Performance of multilayer perceptrons Unsupervised learning Reinforcement learning Recurrent networks and time series analysis Student portal page: http://www.student.chalmers.se/sp/course?course_id=14209 External course webpage: None that currently is alive

Throughout the first year, a seminar series is held in which the students present and discuss recent findings and results reported in the scientific literature. Complex systems science is a large and very heterogeneous research field. When preparing a presentation in the Complex systems seminar course, the students have an opportunity to "dig a little deeper" into a specific area that they find interesting. The students in the class will during the class be listening to 40-50 seminars on different topics, mostly given by their friends in the class but also by the teachers in the program and other guest lecturers. Overall this gives a more fair coverage of complex systems as a field than is possible in the regular classes. In addition the class offers training in a very central skill: oral presentation. Teacher: Claes Andersson Teaching Assistants: Erik Edlund, Oskar Lindgren, Vilhelm Verendel Student portal page: http://www.student.chalmers.se/sp/course?course_id=15014 Course…

The course provides an introduction to modelling macroscopic biological systems. Topics discussed are population dynamics and ecosystems, gene regulation, enzymatic reactions, morphogenesis and pattern formation, and time-series analysis. Student portal page: http://www.student.chalmers.se/sp/course?course_id=14892 External course webpage: http://fy.chalmers.se/~frtbm/ComputationalBiologyA/index.html

The second course in this field aims at giving a basic understanding of computational biology and theoretical models in molecular biology. This will include models of the origin of life, molecular evolution, and molecular genetics. As a consequence of new measurement techniques, our knowledge of structure and function of biological macromolecules has increased significantly in recent years. The amount of data is now so large that it has become necessary to use computational and statistical methods of analysis. The new empirical data now allow statistically significant testing of models for genetic evolution. This has led to a renewed interest in evolution models on the genetic and molecular level. New numerical algorithms and mathematical models have been developed describing population genetics. It is the aim of this course to introduce the mathematical models and computational methods used in the analysis and modelling of genetical data and their evolution. Student portal page:…

The course gives an introduction to the theory and description of nonlinear dynamical systems. How is chaos measured and characterized? How can one control and predict chaotic systems? By using very simple mathematical models, it is possible to obtain chaotic behavior. Through the understanding of such systems, the students will get a feeling for how real systems can behave, learn how to characterize them and in certain special cases how to predict their behavior. Student portal page: http://www.student.chalmers.se/sp/course?course_id=14968 External course webpage: http://fy.chalmers.se/~ostlund/dynamicalsystems

The aim of the course is to give an introduction to fundamental concepts of game theory and to explore the concept of rationality and a series of applications and extensions of game theory. We focus on the effects of individual rationality and also the aggregate behaviour between agents in a large population. What are general principles for rational action? How well does this describe human behavior in practice? The final content of the course can and will be influenced by the students attending it (i.e. other topics may be added to this list). Topics that were covered in last year’s version of the course include: Basic game-theoretic concepts, theory and principles of rational decision-making, backward induction and the rationality paradox, analysis of repeated interaction, tragedy of the commons, evolutionary game theory, public good games, agent-based models in economics, behavioural economics and the environment, bargaining theory and dynamic games. The course…

In this course, we study humanoid robots, i.e. robots that have an approximately human-like shape. Such robots form an important special case of the autonomous robots studied in the course Autonomous agents. For example, unlike wheeled robots, a walking humanoid robot is (in principle) able to climb stairs and is also, in general, better adapted (than a wheeled robot) to environments designed for people. The course begins with two weeks of lectures, during which the theoretical foundations of humanoid robots are explored. The remaining part of the course consists of lab work, during which several different humanoid robots are used for solving a variety of tasks, focusing on human-robot interaction. Student portal page: http://www.student.chalmers.se/sp/course?course_id=17261 External course webpage: http://www.am.chalmers.se/~wolff/Courses/TIF160/

The aim of this course is to give an understanding of fundamental concepts used to describe complex systems. As examples, chaotic low-dimensional systems, self-organizing systems, and cellular automata are discussed. The fact that systems composed of a large number of simple components can exhibit complex phenomena is exemplified in, for example, self-organising systems (in the form of chemical reaction-diffusion systems), the second law of thermodynamics (as a statistical result of large physical systems), neural networks, evolution of cooperation, cellular automata (as an example of an abstract computational class of systems), economic systems of interacting trading agents, urban growth and traffic systems. There is no universal definition of a complex system, but there are several features that are usually brought up when a system is considered complex, typically involving order/disorder and correlations. An important scientific question is whether these and other characteristics of the systems can be quantified in a comprehensive…

The students learn three different simulation methods that are commonly used to model and understand complex systems. The topics covered are agent based modeling, network theory and cellular automata. The example simulations discussed in class range from social systems to physical systems, with many examples from biology. A large part of the course consists of projects where the students work in small groups to implement a small scale simulations. The topics addressed in the project are chosen by the students themselves. An example of an assignment used on the topic of network theory dealt with understanding the core behind Google's ranking of webpages. Student portal page: http://www.student.chalmers.se/sp/course?course_id=15033 Course webpage

 The course begins with a brief survey of classical optimization methods, such as Newton's method, Lagrange multiplier methods, gradient descent etc. Next, three stochastic optimization methods are considered, namely evolutionary algorithms (inspired by darwinian evolution), and ant colony optimization (inspired by the group behavior of ants), and particle swarm optimization (inspired by swarming, as exhibited by, for example, birds and fish). In addition to learning the theoretical background of these algorithms, the students also apply the algorithms in problems of varying degrees of complexity. Student portal page: http://www.student.chalmers.se/sp/course?course_id=14383 External course webpage: http://www.me.chalmers.se/~mwahde/courses/soa/2016/FFR105.html {KomentoDisable}