ellgui
Final year student in the masters program Complex Adaptive Systems at Chalmers University of Technology
Gothenburg
Pinned Repositories
Algorithms-for-machine-learning-and-inference-
This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model. Algorithms of this kind are commonly used in for example classification tasks (character recognition, or to predict if a new customer is creditworthy etc.) and in expert systems (for example for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases. The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature.
Artificial-Neural-Network
Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple processing elements which are connected in a network. Neural networks are increasingly used in the engineering sciences for tasks such as pattern recognition, prediction and control. The theory of neural networks is a inter-disciplinary field (neurobiology, computer science and statistical physics). The course gives an overview and a basic understanding of neural-network algorithms and can develop an understanding of when neural networks are useful in application problems
Computational-Biology-1
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.
Deep-Machine-Learning
The purpose with this course is to give a thorough introduction to deep machine learning, also known as deep learning or deep neural networks. Over the last few years, deep machine learning has dramatically changed the state of the art performance in various fields including speech-recognition, computer vision and reinforcement learning (used, e.g., to learn how to play Go). We focus primarily on basic principles regarding how these networks are constructed and trained, but we also cover many of the key techniques used in different applications. The overall objective is to provide a solid understanding of how and why deep machine learning is useful, as well as the skills to apply them to solve problems of practical importance.
Game-theory-and-rationality
The aim of this course is to give an introduction to game theory and evolutionary models within the field, in order to inspire and engage the students so that they can identify and explore game-theoretic dilemmas or situations during the studies as well as in their future work-life. This is achieved through examining basic game-theoretic concepts including the concept of rationality. The students, typically at the end of their undergraduate studies, are tasked individually as well as in group with acquiring knowledge about a series of game-theoretic applications. We focus on the effects of individual rationality on collective outcomes, as well as the resulting behavior of agents with different strategies in a large population. We cover theory of general principles of rational action and examine known limitations on how well this describes human behavior in reality. Secondary aims include getting hands-on experience of modelling in a game-theoretic context as well as training in reading and presenting scientific articles. The course offers students a possibility to deepen their understanding of their subject area through project-based studies of applications within their respective field.
Simulation-Of-Complex-Systems
The course introduces the students to simulation techniques frequently used in complex systems, emphasising agent based modelling and networks. We discuss examples of applications in physics, biology and social science. The aim of the course is to 1) give the students the level of understanding needed to decide on simulation methodology for a specific problem, 2) define and implement a moderate size simulation project, and 3) evaluate the results from their simulations.
Statistical-Inference
The course gives a deeper understanding of some traditional topics in mathematical statistics such as methods based on likelihood, aspects of experimental design, non-parametric testing, analysis of variance, introduction to Bayesian inference, chi-squared tests, multiple regression.
Statistical-learning-for-big-data
The course should give understanding of and training in techniques for statistical analysis of large data sets.
Stochastic-Optimization-Algorithms
The aim of the course is for the students to attain basic knowledge of new methods in computer science inspired by evolutionary processes in nature, such as genetic algorithms, genetic programming, and artificial life. These are both relevant to technical applications, for example in optimization and design of autonomous systems, and for understanding biological systems, e.g., through simulation of evolutionary processes.
ellgui's Repositories
ellgui/Artificial-Neural-Network
Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple processing elements which are connected in a network. Neural networks are increasingly used in the engineering sciences for tasks such as pattern recognition, prediction and control. The theory of neural networks is a inter-disciplinary field (neurobiology, computer science and statistical physics). The course gives an overview and a basic understanding of neural-network algorithms and can develop an understanding of when neural networks are useful in application problems
ellgui/Algorithms-for-machine-learning-and-inference-
This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model. Algorithms of this kind are commonly used in for example classification tasks (character recognition, or to predict if a new customer is creditworthy etc.) and in expert systems (for example for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases. The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature.
ellgui/Deep-Machine-Learning
The purpose with this course is to give a thorough introduction to deep machine learning, also known as deep learning or deep neural networks. Over the last few years, deep machine learning has dramatically changed the state of the art performance in various fields including speech-recognition, computer vision and reinforcement learning (used, e.g., to learn how to play Go). We focus primarily on basic principles regarding how these networks are constructed and trained, but we also cover many of the key techniques used in different applications. The overall objective is to provide a solid understanding of how and why deep machine learning is useful, as well as the skills to apply them to solve problems of practical importance.
ellgui/Statistical-learning-for-big-data
The course should give understanding of and training in techniques for statistical analysis of large data sets.
ellgui/Stochastic-Optimization-Algorithms
The aim of the course is for the students to attain basic knowledge of new methods in computer science inspired by evolutionary processes in nature, such as genetic algorithms, genetic programming, and artificial life. These are both relevant to technical applications, for example in optimization and design of autonomous systems, and for understanding biological systems, e.g., through simulation of evolutionary processes.
ellgui/Computational-Biology-1
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.
ellgui/Game-theory-and-rationality
The aim of this course is to give an introduction to game theory and evolutionary models within the field, in order to inspire and engage the students so that they can identify and explore game-theoretic dilemmas or situations during the studies as well as in their future work-life. This is achieved through examining basic game-theoretic concepts including the concept of rationality. The students, typically at the end of their undergraduate studies, are tasked individually as well as in group with acquiring knowledge about a series of game-theoretic applications. We focus on the effects of individual rationality on collective outcomes, as well as the resulting behavior of agents with different strategies in a large population. We cover theory of general principles of rational action and examine known limitations on how well this describes human behavior in reality. Secondary aims include getting hands-on experience of modelling in a game-theoretic context as well as training in reading and presenting scientific articles. The course offers students a possibility to deepen their understanding of their subject area through project-based studies of applications within their respective field.
ellgui/Simulation-Of-Complex-Systems
The course introduces the students to simulation techniques frequently used in complex systems, emphasising agent based modelling and networks. We discuss examples of applications in physics, biology and social science. The aim of the course is to 1) give the students the level of understanding needed to decide on simulation methodology for a specific problem, 2) define and implement a moderate size simulation project, and 3) evaluate the results from their simulations.
ellgui/Statistical-Inference
The course gives a deeper understanding of some traditional topics in mathematical statistics such as methods based on likelihood, aspects of experimental design, non-parametric testing, analysis of variance, introduction to Bayesian inference, chi-squared tests, multiple regression.