Search result: Catalogue data in Spring Semester 2019
Data Science Master | ||||||
Interdisciplinary Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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701-1226-00L | Inter-Annual Phenomena and Their Prediction | W | 2 credits | 2G | C. Appenzeller | |
Abstract | This course provides an overview of the current ability to understand and predict intra-seasonal and inter-annual climate variability in the tropical and extra-tropical region and provides insights on how operational weather and climate services are organized. | |||||
Objective | Students will acquire an understanding of the key atmosphere and ocean processes involved, will gain experience in analyzing and predicting sub-seasonal to inter-annual variability and learn how operational weather and climate services are organised and how scientific developments can improve these services. | |||||
Content | The course covers the following topics: Part 1: - Introduction, some basic concepts and examples of sub-seasonal and inter-annual variability - Weather and climate data and the statistical concepts used for analysing inter-annual variability (e.g. correlation analysis, teleconnection maps, EOF analysis) Part 2: - Inter-annual variability in the tropical region (e.g. ENSO, MJO) - Inter-annual variability in the extra-tropical region (e.g. Blocking, NAO, PNA, regimes) Part 3: - Prediction of inter-annual variability (statistical methods, ensemble prediction systems, monthly and seasonal forecasts, seamless forecasts) - Verification and interpretation of probabilistic forecast systems - Climate change and inter-annual variability Part 4: - Challenges for operational weather and climate services - Role of weather and climate extremes - Early warning systems - A visit to the forecasting centre of MeteoSwiss | |||||
Lecture notes | A pdf version of the slides will be available at Link | |||||
Literature | References are given during the lecture. | |||||
701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation | W | 3 credits | 2V + 1U | D. N. Bresch, R. Knutti | |
Abstract | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. | |||||
Objective | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course. | |||||
Content | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources | |||||
Lecture notes | Powerpoint slides will be made available | |||||
Literature | - | |||||
Prerequisites / Notice | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence basic understanding of scientific programming forms a prerequisite of the course. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung) | |||||
851-0252-06L | Introduction to Social Networks: Theory, Methods and Applications This course is intended for students interested in data analysis and with basic knowledge of inferential statistics. | W | 3 credits | 2G | C. Stadtfeld, T. Elmer, A. Vörös | |
Abstract | Humans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines. | |||||
Objective | The aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3). In particular, at the end of the course students will - Know the fundamental theories in social networks research (1) - Understand core concepts of social networks and their relevance in different contexts (1, 3) - Be able to describe and visualize networks data in the R environment (2) - Understand differences regarding analysis and collection of network data and other type of survey data (2) - Know state-of-the-art inferential statistical methods and how they are used in R (2) - Be familiar with the core empirical studies in social networks research (2, 3) - Know how network methods can be employed in a variety of scientific disciplines (3) | |||||
363-1091-00L | Social Data Science | W | 3 credits | 2G | D. Garcia Becerra | |
Abstract | Social Data Science is introduced as a set of techniques to analyze human behavior and social interaction through digital traces. The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective. | |||||
Objective | A successful participant of this course will be able to - understand a wide variety of techniques to retrieve digital trace data from online data sources - store, process, and summarize online data for quantitative analysis - perform statistical analyses to test hypotheses, derive insights, and formulate predictions - implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization - interpret the results of data analysis with respect to theoretical and testable principles of human behavior - understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity | |||||
Content | Social Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data. SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior. Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression. These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior. The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects. The course will cover various examples of the application of SDS: - Search trends to measure information seeking - Popularity and social impact - Evaluation of sentiment analysis techniques - Quantitative analysis of emotions and social media sharing - Twitter social network analysis The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective. The block course contains lectures and exercise sessions during the morning and afternoon of five days. Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures. | |||||
Lecture notes | The lecture slides will be available on the Moodle platform, for registered students only. | |||||
Literature | See handouts. Specific literature is provided for download, for registered students only. | |||||
Prerequisites / Notice | Participants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective. Prior knowledge of advanced R, information retrieval, or information systems is not necessary. Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform the guided exercises. Course evaluation is based on the project developed in the last session datathon (50%) and on the final project report (50%). The course takes place between Feb 11th and Feb 15th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00. | |||||
227-0395-00L | Neural Systems | W | 6 credits | 2V + 1U + 1A | R. Hahnloser, M. F. Yanik, B. Grewe | |
Abstract | This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experimental techniques used in studies of animal behavior and underlying neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on efforts to design artificially intelligent systems. | |||||
Objective | This course introduces - Methods for monitoring of animal behaviors in complex environments - Information-theoretic principles of behavior - Methods for performing neurophysiological recordings in intact nervous systems - Methods for manipulating the state and activity in selective neuron types - Methods for reconstructing the synaptic networks among neurons - Information decoding from neural populations, - Sensorimotor learning, and - Neurobiological principles for machine learning. | |||||
Content | From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics. | |||||
Prerequisites / Notice | Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L). As part of the exercises for this class, students are expected to complete a (python) programming project to be defined at the beginning of the semester. | |||||
227-0973-00L | Translational Neuromodeling | W | 8 credits | 3V + 2U + 1A | K. Stephan | |
Abstract | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises. | |||||
Objective | To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | |||||
Content | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal mechanisms from neuroimaging data, and hierarchical Bayesian models for inference on cognitive mechanisms from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models or inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is permitted. | |||||
Literature | See TNU website: Link | |||||
Prerequisites / Notice | Knowledge of principles of statistics, programming skills (MATLAB or Python) | |||||
227-1032-00L | Neuromorphic Engineering II Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 credits | 5G | T. Delbrück, G. Indiveri, S.‑C. Liu | |
Abstract | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I". | |||||
Objective | Design of a neuromorphic circuit for implementation with CMOS technology. | |||||
Content | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester. | |||||
Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation. | |||||
Prerequisites / Notice | Prerequisites: Neuromorphic Engineering I strongly recommended | |||||
227-1034-00L | Computational Vision (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: Link | W | 6 credits | 2V + 1U | D. Kiper | |
Abstract | This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision. | |||||
Objective | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||
Content | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||
Literature | Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995. | |||||
851-0739-01L | Building a Robot Judge: Data Science For the Law Particularly suitable for students of D-INFK, D-ITET, D-MTEC | W | 3 credits | 2V | E. Ash | |
Abstract | This course explores the automation of decisions in the legal system. We delve into the tools from natural language processing and machine learning needed to predict judge decision-making and ask whether it is possible -- or even desirable -- to build a robot judge. | |||||
Objective | Is a concept of justice what truly separates man from machine? Recent advances in data science have caused many people to reconsider their responses to this question. With expanding digitization of legal data and corpora, alongside rapid developments in natural language processing and machine learning, the prospect arises for automating legal decisions. Data science technologies have the potential to improve legal decisions by making them more efficient and consistent. The benefits to society from this automation could be significant. On the other hand, there are serious risks that automated systems could replicate or amplify existing legal biases and rigidities. This course introduces students to the data science tools that are unlocking legal materials for computational and scientific analysis. We begin with the problem of representing laws as data, with a review of techniques for featurizing texts, extracting legal information, and representing documents as vectors. We explore methods for measuring document similarity and clustering documents based on legal topics or other features. Visualization methods include word clouds and t-SNE plots for spatial relations between documents. We next consider legal prediction problems. Given the evidence and briefs in this case, how will a judge probably decide? How likely is a criminal defendant to commit another crime? How much additional revenue will this new tax law collect? Students will investigate and implement the relevant machine learning tools for making these types of predictions, including regression, classification, and deep neural networks models. We then use these predictions to better understand the operation of the legal system. Under what conditions do judges tend to make errors? Against which types of defendants do parole boards exhibit bias? Which jurisdictions have the most tax loopholes? In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. Some programming experience in Python is required, and some experience with text mining is highly recommended. | |||||
851-0739-02L | Building a Robot Judge: Data Science for the Law (Course Project) This is the optional course project for "Building a Robot Judge: Data Science for the Law." Please register only if attending the lecture course or with consent of the instructor. Some programming experience in Python is required, and some experience with text mining is highly recommended. | W | 2 credits | 2V | E. Ash | |
Abstract | Students investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. | |||||
Objective | ||||||
Content | Students will investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. We will use these predictions to better understand the operation of the legal system. In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question. | |||||
851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 3.3.2019 at the latest. | W | 3 credits | 2S | S. Bechtold, T. Roscoe, E. Vayena | |
Abstract | This course introduces students to societal perspectives on the big data revolution. Discussing important contributions from machine learning and data science, the course explores their legal, economic, ethical, and political implications in the past, present, and future. | |||||
Objective | This course is intended both for students of machine learning and data science who want to reflect on the societal implications of their field, and for students from other disciplines who want to explore the societal impact of data sciences. The course will first discuss some of the methodological foundations of machine learning, followed by a discussion of research papers and real-world applications where big data and societal values may clash. Potential topics include the implications of big data for privacy, liability, insurance, health systems, voting, and democratic institutions, as well as the use of predictive algorithms for price discrimination and the criminal justice system. Guest speakers, weekly readings and reaction papers ensure a lively debate among participants from various backgrounds. | |||||
363-1100-00L | Risk Case Study Challenge Limited number of participants. Please apply for this course via the official website (Link). Once your application is confirmed, registration in myStudies is possible. | W | 3 credits | 2S | B. J. Bergmann, A. Bommier, S. Feuerriegel | |
Abstract | This seminar provides master students at ETH with the challenging opportunity of working on a real risk modelling and risk management case in close collaboration with a Risk Center Partner Company. For the Spring 2019 Edition the Partner will be Zurich Insurance Group. | |||||
Objective | Students work on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the risk-related cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar students visit the partners’ headquarters, conduct interviews with members of the management team as well as internal and external experts, and present their results. | |||||
Content | Get a basic understanding of o The insurance and reinsurance business o Risk management and risk modelling o The role of operational risk management Get in contact with industry experts and conduct interviews on the topic. Conduct a small empirical study and present findings to the company | |||||
Prerequisites / Notice | Please apply for this course via the official website (Link). Apply no later than February 15, 2019. The number of participants is limited to 14. |
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