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Courses

On this page you will find an overview of available online and offline courses relevant to my research. Most of the online courses are offered for free on coursera, which has become an excellent platform for open teaching.

Online courses

Networks – Theory and Application

  • Lecturer: Lada Adamic (Senior Researcher at U Michigan)
  • Year: 2012
  • Description: The course covers topics in network analysis, from social networks to applications in information networks such as the Internet. I will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval.
  • Website: http://open.umich.edu/education/si/si508/fall2008

Ruby – Programming for Beginners

Machine Learning

  • Lecturer: Andrew Ng, Associate Professor
  • Year: 2012
  • Description: This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
  • Website:https://www.coursera.org/course/ml

Probabilistic Graphical Models

  • Lecturer: Daphne Koller, Professor
  • Year: 2012
  • Description: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.
  • Website:https://www.coursera.org/course/pgm

Game Theory

  • Lecturer: Matthew O. Jackson, Yoav Shoham
  • Year: 2012
  • Description:  The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We’ll include a variety of examples including classic games and a few applications.
  • Website:https://www.coursera.org/course/gametheory

Model Thinking

  • Lecturer: Scott E. Page
  • Year: 2012
  • Description: In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians.The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life.
  • Website: https://www.coursera.org/course/modelthinking

Software Engineering

  • Lecturer:Armando Fox, David Patterson
  • Year: 2012
  • Description: This course teaches the engineering fundamentals for long-lived software using the highly-productive Agile development method for Software as a Service (SaaS) using Ruby on Rails. Agile developers continuously refine and refactor a working but incomplete prototype until the customer is happy with result, with the customer offering continuous feedback. Agile emphasizes user stories to validate customer requirements; test-driven development to reduce mistakes; biweekly iterations of new software releases; and velocity to measure progress. We will introduce all these elements of the Agile development cycle, and go through one iteration by adding features to a simple app and deploying it on the cloud using tools like Github, Cucumber, RSpec, SimpleCov, Pivotal Tracker, and Heroku.
  • Website: https://www.coursera.org/course/saas

Networked Life

  • Lecturer: Michael Kearns
  • Year: 2012
  • Description:  What science underlies companies like Facebook, Twitter and Google? How does your position in a social network (dis)advantage you? What do game theory and the Paris subway have to do with Internet routing? How might a social network influence election outcomes? What are the economics of email spam? How does Google find what you’re looking for… and exactly how do they make money doing so?
  • Website: https://www.coursera.org/course/networks

Natural Language Processing

  • Lecturer:  Dan Jurafsky, Professor Christopher Manning
  • Year: 2012
  • Description: This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.
  • Website: https://www.coursera.org/course/nlp

Offline Courses

The Structure of Social and Information Networks

  • Lecturer: Dr. Maria Grineva (maria.grineva @ inf.ethz.ch)
  • Year: 2012
  • Description: This introductory course takes an interdisciplinary look at how the social, technological, and natural worlds are connected. The course will cover recent research on the structure and analysis of large social and information networks and on models and algorithms that abstract their basic properties. Class will explore how to practically analyze large scale network data and how to reason about it through models for network structure and evolution. Topics include methods for link analysis and network community detection, diffusion and information propagation on the Web, modeling network traffic and behavior using game theory, market and strategic interaction in networks.
  • Website: The Structure of Social and Information Networks

Networks

  • Lecturer: David Easley (Economics) and Jon Kleinberg (Computer Science)
  • Year:  2007
  • Description: (1) Graph Theory and Social Networks (2) Game Theory (3) Markets and the Network Structure of Strategic Interaction (4) The World-Wide Web and Information Access (5) Network Effects (6) Diffusion and Contagion in Networks (7) Further Applications of Network Analysis
  • Website: http://www.infosci.cornell.edu/courses/info204/2007sp/

Networks

Social Network Analysis

Social and Technological Network Analysis

Social Network Analysis and Organization Behavior

6th UK Social Networks Workshop

Networked Life

  • Lecturer: Prof. Michael Kearns 
  • Year: 2011
  • Description: What science underlies the companies above? What are the economics of email spam? Why do some social networking services take off, and others die? What do game theory and the Paris Subway have to do with Internet routing? How does Google find what you’re looking for… and exactly how do they make money doing so? What structural properties might we expect any social network to have? How might a social network influence election outcomes? What problems can be solved by  rowdsourcing? How does your position in a social network (dis)advantage you?
  • Website: http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/

Web Science and Web Technology

  • Lecturer: Dr. Markus Strohmaier
  • Year: 2009
  • Description: This course aims to provide students with a basic knowledge and understanding about the structure and analysis of selected web phenomena and technologies. Topics include the small world problem, network theory, social network analysis, graph search and technologies/standards/architectures such as JSON, RDF, REST and others.
  • Website: http://kmi.tugraz.at/staff/markus/courses/SS2009/707.000_web-science/

Social Network analysis with Pajek

  • Lecturer: Andrej Mrvar
  • Year: 2010
  • Description:This course covers general topics of network analysis. The emphasis is given to visualization of networks and analysis of very large networks. (Make sure to also read the newly published book on pajek –> see book section)
  • Website: http://mrvar2.fdv.uni-lj.si/lectures.htm

The Network Society | Innovations in networks and alliance management | Social entrepreneurship

  • Lecturer: Uwe Matzat, Rudi Bekkers, Gerrit Rooks
  • Year: 2011/2012
  • Description: In this course we consider the theory and empirics of innovation and technological change from a network perspective. In what kind of networks can innovation and technological change prosper? The course considers how networks, including personal networks, can hamper or facilitate innovation and technological change, focusing primarily at the meso- and micro level. Innovation processes as well as knowledge and technology spillovers are driven by the interactions between actors such as producers, suppliers, customers, knowledge institutes, and are constrained by institutional arrangements and market structures. Over time, networks between these actors evolve. The shape and structure of these networks affect the ease with which innovation processes and technology spillovers occur and develop.
  • Website: http://www.tue-tm.org/INAM/

Social Networks

Methoden der Netzwerkanalyse

Teaching about Social Networks

  • Compiled by: D.B. Tindall and Todd E. Malinick
  • Description: Tindall and Malinick have compiled an overview of sylabi, assignments and other resources that have been tought in offline courses in the last 10 years. Their paper is a great resource if you want to create a course yourself.
  • UNDERGRADUATE LEVEL:
  • Network Analysis Daniel McFarland
  • Social Network Analysis Ed Collom
  • Social Network Analysis Robert Hanneman
  • Six Degrees: The New Science of So cial Networks Duncan Watts
  • Introduction to Social Network Analysis Noah Friedkin
  • Social Networks Keith Hampton
  • Structural Analysis Alexandra Marin
  • Relations and Networks of Organizations Joerg Raab
  • Social Network Analysis David Gartrell
  • GRADUATE COURSE SYLLABI
  • Social Network Analysis: Theory & Methods David Knoke
  • Analysis of Social Network Data Katherine Faust
  • Social Networks Seminar Noah Friedkin
  • Network Methods A Phillip Bonacich
  • Network Methods B Phillip Bonacich
  • Social Network Analysis Joseph Galaskiewicz
  • Social Networks Seminar Ken Frank
  • Seminar on Social Networks James Moody
  • Social Networks and Information Caroline Haythornwaite
  • Social Networks Christina Prell
  • Communication and Social Networks George Barnett
  • Social Network Analysis Thomas Valente
  • Dynamic Network Analysis Kathleen Carley
  • Interorganizational Relationships: Research Master Joerg Raab
  • Statistical Analysis of Networks Mark S. Handcock
  • Download: Tindall and Malinick 2008 (drop them or me an email, I will have to check if I am allowed to re-distribute their pdf to others)

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