Information Science Classes (Computer Science, Mathematics, ISIS)

Social Science (Sociology, Anthropology)

Physical Science

Graduate Level Courses

Upper Level Undergraduate

General Audience

Interdisciplinary Courses

Social Networks

Economic Networks

Technological Networks (Internet etc)

Biological Networks

Network Analysis (algorithms, data analysis)

Information Retrieval

Google: The Computer Science Within and its Impact on Society taught by Shivnath Babu at Duke University.

Course Description: This class uses Google as a platform for understanding basic concepts in Computer Science. The course aims to explain the ideas of information retrieval, databases, distributed systems, human computer interaction, artificial intelligence, and data mining. The goals of the course are to look at the technology behind the google system and then apply the impacts to social, economic and ethical settings. The class is a seminar that focuses heavily on reading and class discussion.

Syllabus.

Intended Audience: This is a freshman seminar class. This means that the class is only open to first year students and there is a cap on the class of sixteen students. Non-majors only.

Prerequisites: The only prerequisite is that students must be in their first year of school. The class is looking to attract students with little background in computer science and introduce them to the discipline.

Textbooks and Articles: This class has one main textbook, several reference texts and supplementary articles.

Assignments: The grade breakdown is as follows: 20% for class participation, 20% for leading a class discussion on an assigned reading, 20% for quizzes and 40% for written assignments.

Seminar on Social Networks taught by James Moody at Duke University.

Course Description: This seminar focuses on theoretical and substantive themes within social network analysis. The theoretical heart of this approach to social science is that actors are interdependent, and that social structure emerges from regularities in this interdependence. In this seminar, we will couple the substantive and theoretical development of social network analysis with methodological tools to implement network research. By the end of the course, you should (1) know the major theoretical ideas supporting network research, (2) be able to collect social network data and, (3) be able to analyze and interpret social network data. Social network research is unique in the extent to which methodological tools derive directly from substantive theories. As such, class time will be split almost 50-50 on methodological and substantive (theory, application, and examples) issues, with each substantive topic tied to a new method or analysis strategy. Substantive topics will include work on sexual behavior, organizational performance, delinquency, power, friendship, and much more.

Syllabus

Intended Audience: This is a graduate student course in sociology.

Prerequisites: There are no explicit prerequisites.

Textbooks and Articles: The main text for the class is Wasserman and Faust (1994): Social Network Analysis. Cambridge University Press. This book will provide the main methodological and background reading for the course. You should also read either: Linked: The New Science of Networks by Albert-László Barabási, as an introduction to the science of networks or The Development of Social Network Analysis by Linton Freeman, on the history of SNA. There are also papers assigned on the website for every topic to be discussed.

Assignments:The main requirement of this seminar is a research paper that uses the methods or ideas of social network analysis. This may be a revision of previous work (an MA paper, another course paper, etc.) or a new paper. If this is a revision of a previous paper, you need to show that the addition of network ideas or methods significantly contributes to the revision. You may collaborate with up to 2 other students (3-authors total) on your final paper. The second requirement for the class is a set of homework assignments designed to build familiarity with the software and analysis techniques. Assignments will be largely self-graded, with the solutions posted on the web page. Finally, since this is a seminar, in-class participation is necessary. The requirement breakdown will be roughly as follows: 65% paper, 30% homeworks, 5% class participation.

Information Technology taught by Jennifer Golbeck at the University of Maryland, College Park

Course Description: Introduction to the application of computer hardware, software, and information systems for the provision of information services. Special emphasis on the design of systems for serving information needs.

Course Goals:

Syllabus

Intended Audience: This is an information Science class, with some emphasis on technology, however there are no explicit prerequisites. It appears that the class is aimed at those with a background and or interest in computer science, but it is basic enough for anyone who wants to try.

Prerequisites: No specific prerequisites.

Textbooks and Articles: The textbook for this class is Discovering Computers 2007: A Gateway to Information by Gary B. Shelly, Thomas J. Cashman, Misty E. Vermaat Course Technology ISBN:1-4188-4370-9

Assignments: The assignments are broken down as follows:

Online Social Networks taught by Fred Stutzman at UNC School of Information and Library Science (Graduate Program).

Course Description: This course is a primer on the study of online social networks. We will explore the theory, methods and findings of a growing literature on the topic. We will also explore applications and use cases, particularly in the context of education and library/information services. While online social networks are but a subset of social software, this course should provide you a strong set of fundamentals for exploring the multiple facets of our pervasive online sociality.

Syllabus

Intended Audience: This is a graduate level class at the UNC School of Information and Library Science. There is no mathematic background required, so the class is open to all graduate students.

Prerequisites: Graduate Student status.

Suggested Textbook: Vogt, W. Paul (2005). Dictionary of Statistics and Methodology: A Nontechnical Guide for the Social Sciences. Thousand Oaks, CA: Sage Publications.

Past Textbooks and Articles

Assignments: First, must contribute a question, observation or enhancement to online forum each week (10%). Second, must produce a topical issues presentation once during the semester, as well as act as a discussion facilitator on presentation day (10%). Third, complete a term project (50% paper, 10% proposal/outline/bibliography). There is also 20% grade for class participation. All of the specific assignments are explained in more detail on theSyllabus.

Networked LifeTaught by Prof. Michael Kearns at University of Pennsylvania Dept. of Computer and Information Science

Course description: The Networked Life class looks at connections between seemingly unrelated parts of the world using social, economic, strategic and technological networks. The class tries to determine how these different networks are related and why that is important to everyday life. Networked Life will explore recent scientific efforts to explain social, economic and technological structures, and the way these structures interact, on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. The class looks at these relationships mathematically, but does not require a strong math background. The class will engage in a series of communal experiments in distributed human decision-making on networks, and analyze topics such as how network structure influences individual behavior and collective outcomes. The two new topics that will be covered are economic models of network formation and sponsored searches.

Syllabus

Intended Audience: This courses is open to all students, all majors and all levels and is intended for a varied audience. There are no programming skills required, but students must feel comfortable using a computer.

Prerequisites: There are no prerequisites for this course.

Textbooks and articles: There are three books required for this class. There are also several articles that are posted on the course website throughout the semester.

Assignments: The assignments involve reading the above books and articles, a few problem sets, whose links are on the syllabus above and participation in behavioral network science experiments. There was also a midterm and a final exam.

The Structure of Information Networks by Prof. Jon Kleinberg at Cornell University Dept. of Computer Science

Course Description: This course covers recent research on algorithms for analyzing networks, and models that abstract their basic properties. Topics include combinatorial and probabilistic techniques for link analysis, centralized and decentralized search algorithms, network models based on random graphs, and connections with work in the social sciences.

Syllabus

Intended Audience: This is an upper level computer science course and requires knowledge in mathematics. It is not for a general audience.

Prerequisites: The course prerequisites include introductory-level background in algorithms, graphs, probability, and linear algebra.

Textbooks and Articles: This course does not have a set text book but involves a substantial amount of reading in article form. There are links to all of the articles on the course webpage/syllabus.

Assignments: two problem sets, a short reaction paper, and a more substantial project, all of which are described in detail here.

Networks and Complexity in Social Systems by Prof. Duncan Watts at Columbia University Dept. of Sociology

Course description: The primary aim of this course is to describe a unified theoretical framework for addressing network dynamics problems in the social sciences. This class is meant to investigate the complex structure of networks and how their components interact. The class also involves data analysis of these networks. The course material is drawn from graph theory, statistical physics, nonlinear dynamics, and computer science, as well as from social networks theory, and focuses on the empirical description of real networks, as well as sociologically relevant phenomena such as disease propagation, search, and the diffusion of innovations. The class is highly interdisciplinary.

Syllabus

Intended audience: This is a sociology class, so it is not restricted to people with a knowledge of computer science. The class looks more at the theory and literature on the subject that actual problem solving.

Prerequisites: There are no prerequisites, but a knowledge of mathematics is helpful.

Textbook and Articles: This class relies solely on articles. Only the starred articles on the syllabus are required, but a great deal of additional material is provided for the more advanced students.

Assignments: There is no indication on the website or the syllabus as to the nature of the assignments.

Social Network Analysis by Prof. Barry Wellman at University of Toronto Dept. of Sociology

Course Description: This course focuses on the analysis of social networks. The class looks at the relationships between people, organizations and interest groups and how these relations are structured. The class looks at networks both quantitatively and qualitatively, but has a primarily sociological focus. The class also looks at the interaction of social and computer networks and the effects of economics and politics on networks.

Syllabus

Intended Audience: This is a graduate level course in sociology.

Prerequisites: There are no prerequisites for this class.

Textbooks and Articles: There are several books listed as being “useful” for the class. There are also several articles assigned under each section of the syllabus which can be found here.

Assignments: The major grade for this class is a research paper. The grade breakdown is as follows: 10% for a presentation on one of the readings, 10% class participation, 10% reaction papers due at each class meeting, 5% paper proposal, 25% paper first draft, 40% final paper.

Networks and Complexity by Prof. Douglas White at University of California, Irvine Dept. of Anthropology

Course Description: This is an anthropology course that looks at social networks and their interactions from a social science point of view. The course looks at methods of dynamical analysis, the grounding of complexity theory in the sciences, the contributions of evolutionary game theory and coevolutionary theory. The course also aims to teach how computer programs are used to do network analysis and/or simulate multi-agent interaction. The class works from a variety of articles that use a dynamical, network or complex systems approach.

Syllabus

Intended audience: This is an anthropology class, for a social science audience that seems to be open to all students.

Prerequisites: There are no apparent prerequisites for this class.

Textbook and Articles: There are four books listed for this class. There are also several supplementary books listed on the course website.

Assignments: The grade breakdown can be found here. The main assignments are a report on one of the readings and a research paper/class presentation.

Algorithms, Game Theory, & the Internet by Prof. Christos Papadimitriou at University of California, Berkeley Computer Science Division

Course Description: This class aims to use game theory and economics to look at networks, specifically the internet. Some of the suggested topics include: nash equilibrium, refinements of equilibrium concepts, social choice theory, mechanism design, multicast pricing, worst-case equilibria, combinational auctions, evolutionary game theory, fairness in cooperative games and economic aspects of the internets graph, privacy and clustering.

Syllabus

Intended audience: This is a computer science course that uses mathematical theory. It is a seminar intended for an upper level audience.

Prerequisites: There are no stated prerequisites but it appears to require a knowledge of math.

Textbooks and Articles: There are two recomended books. There are also an extensive number of articles on the course website that correspond with different lectures.

Assignments: 2-3 problem sets. Students are also required to select a topic (among those pertinent to the scope of the course but not covered in the course) read the literature on it, and write an appraisal that is as original as possible.

Update: A more recent version of the class and its website can be found here. The new site is less informative but it does center on one new book: Algorithmic Game Theory by Nisan, Roughgarden, Tardos, and V. Vazirani

Graphs and Networks in Systems Biology by Professor Reka Albert at Pennsylvania State University

Course Description: This class looks at networks and graphs and applies this knowledge to an understanding of biology. The class touches on elements of graph theory, random graph theory, network models/theory, network robustness, cellular networks, modeling reaction networks, signal transduction networks and gene regulatory networks. Basically the class begins looking at networks in a general way and moves towards a focus in the natural sciences.

Syllabus

Intended audience: This is a physics/biology course that seems geared toward natural scientists, not computer scientists.

Prerequisites: MATH 017 (finite mathematics) or similar introduction to probabilities and combinatorics; MATH 110 (techniques of calculus) or MATH 140B (calculus and biology) or similar introduction to calculus; BIOL 011 or similar introductory biology; BMB 211 or similar elementary biochemistry

Textbooks and Articles: Although there are no specific readings assigned for each class there is a lot of supplementary reading given to help people who do not have the background required for the course. These materials were also suggested to be used towards their final projects.

Older Textbooks and Articles

Assignments: There are homework assignments due every week (10 total) worth 40% of the grade and a research project/presentation worth 60% of the total grade.

Network Theory By Professor Mark Newman at the University of Michigan.

Course description: This course will introduce and develop the mathematical theory of networks, particularly social and technological networks, with applications to network-driven phenomena in the Internet, search engines, network resilience, epidemiology, and many other areas. Topics to be covered will include experimental studies of social networks, the world wide web, information and biological networks; methods and computer algorithms for the analysis and interpretation of network data; graph theory; models of networks including random graphs, preferential attachment models, and the small-world model; network dynamics. As a side note an older course website also has a very interesting section of links to related classes at other schools (some, but not all of which are a part of this survey!)

Syllabus

Intended audience: This class is intended for an audience with a background in math and/or computer science.

Prerequisites: The class requires students to have a background in calculus and linear algebra. A background in programming is not required, but is recommended.

Textbooks and articles: This class does not have a textbook, but it does have a course-pack made up of various articles and studies. There is also a list of recommended books to help students brush up on the material.

Assignments: The class has weekly graded problem sets (35%), one mid-term exam (30%) and one final exam (35%).

Scaling in Networks by Professor Aurel Lazar at Columbia University.

Course Description: This is a graduate level research seminar which focuses on evolutionary models of the internet with a focus on scalability. The course looks at random graph models of the internet and analyzes their growth and evolution. Some of the other topics covered are as follows: The topological structure of networks arising in communications, biology, social sciences and economics. Structural characteristics of evolving networks. Topological invariants obtained through empirical investigations: power laws, clustering and the small world phenomena. Classical random networks and elements of random graph theory. Percolation theory. Small world networks and their properties. Scale-free networks and their characteristics. Models of evolving networks. Error and attack tolerance, and the robustness of the Internet.

Syllabus

Intended Audience: This is a graduate level class for computer science students.

Prerequisites: A graduate level course in computer communication networks (e.g., ELEN E6761) or a course in stochastic processes (e.g., ELEN E6711), or the instructor’s approval.

Textbook and Articles: This class does not have a textbook but involves reading of research papers that were to be posted on the web. The instructor lists the following as reference texts.

Assignments: Students are expected to submit a review paper on one of the following scalability topics on communication networks: scalability of switching systems, network protocols, routing and qos architectures, wireless networks, content delivery networks, signaling architectures, management architectures, peer-to-peer and other service architectures. Each student is also expected to present one of the research papers to be discussed in class. Students are expected to complete during the course of the semester either a programming project that focuses on graph invariants arising in modeling the architecture of the Internet or a theoretical analysis on the implications of graph invariants on the architecture of the Internet. Class presentation and participation 1/3, midterm review paper 1/3, final project 1/3.

Structural Data Mining By Professor Katy Borner at the University of Indiana.

Course Description: This course introduces students to major methods, theories, and applications of structural data mining and modeling. It covers elementary graph theory and matrix algebra, data collection, structural data mining, data modeling, and applications. Upon taking this course students will be able to analyze and describe real networks (power grids, WWW, social networks, etc.) as well as relevant phenomena such as disease propagation, search, organizational performance, social power, and the diffusion of innovations.

Syllabus

Intended Audience: This is an upper level information science class. The class works with topics in computer science, math, information science and natural science and thus seems structured for a very specific audience. The work load and specificity of material seem to indicate that this class is only for an advanced audience.

Prerequisites: L401 (this seems to be a more elementary Information Science class, however its website is no longer active).

Textbooks and Articles: There is no assigned textbook for this class, only weekly articles that involve close reading and class presentations. There is also a wealth of extra materials for each topic on the course website to help with projects. All of the assigned readings are linked through the syllabus.

Assignments: class participation (20%), presentation of selected readings (10%), small projects (30%), and final project (40%). The small projects are as follows:

Networks By A. Boudourides at the University of Patras (Greece).

Course Description: This course offers an overview of the theory of complex networks. The course uses different perspectives from mathematics as well as sociology, economics, biology and physics to look at networks. The course website also goes into detail about the six main areas of interest: First, we are going to study the ‘small world’ properties, which in a sense place complex networks somewhere in between regular lattices and random graphs. Although small world networks have been known to social scientists since the 1960s, during the last years they have been found in a growing number of different cases, as in the Web and networks of scientific collaboration. Moreover, two important characteristics found in complex networks are that these networks are scale-free and that various attributes are distributed on these networks according to non-linear power laws. In this way, studying the graph structure of the Internet, one is able to discern that these types of non-linear behavior abound in a broad range, from the Internet topology to the World-Wide Web and e-mail networks. By understanding the structure and the dynamics of complex networks, one is able to implement more efficient methods and algorithms of search on such networks. Finally, we are interested in analyzing a group of diffusion phenomena occurring on complex networks, as in epidemics, viruses, spamming etc.; thus, we intend to study those mechanisms generating heterogeneous patterns over the network and implying their propagation or effacement or emergence of diverse equilibrium patterns. This course also has links on it’s website to other similar courses.

Syllabus

Intended Audience: This class is only open to graduate students in mathematics.

Prerequisites: Must have strong mathematical background.

Textbooks and articles: There is no textbook for this class. The required articles for this class are marked on the syllabus with a star. There are also supplementary readings on every topic.

Assignments: There are no given assignments on the course website. However, it would seem to involve the close reading and analysis of the assigned articles.

Information Retrieval By Dragomir R. Radev at the University of Michigan.

Course Description: This class gives an overview of the concepts behind information retrieval and then goes into more detail describing the current state of the field. The class looks at how to build a search engine and then how they work. The other topics covered include: preprocessing, Stemming, Document representations, TF*IDF, Indexing and Searching, Inverted indexes, IR Models, The Vector model, The Boolean model, Retrieval Evaluation, Precision and Recall, F-measure, Reference collections, The TREC conferences, Queries and Documents, Query Languages, Natural language querying, Word distributions, The Zipf distribution, Relevance feedback and query expansion, Approximate matching, Compression, Vector space similarity and clustering, k-means clustering, Document classification, k-nearest neighbors, Naive Bayes, Support vector machines, Singular value decomposition and Latent Semantic Indexing, Probabilistic models, Document models, Language models, Crawling the Web, Hyperlink analysis, Measuring the Web, Hypertext retrieval, Web-based IR, Social network analysis for IR, Hubs and authorities, PageRank and HITS, Focused crawling, Resource discovery, Discovering communities, Collaborative filtering, Information extraction using Hidden Markov Models, relevance transfer, XML retrieval, text tiling, text summarization, question answering.

Syllabus

Intended Audience: This is an upper level class in Computational Linguistics and would seem to be geared towards a student with a computer science background.

Prerequisites: There are no explicitly stated prerequisites, but this course seems to be intended for an audience with prior knowledge of computational science.

Textbooks and articles: This course has two required books and several required papers.

Assignments: The course will have three homework assignments in the form of problem sets. Each problem set will include essay-type questions, problems designed to show understanding of specific concepts, and hands on exercises involving Information Retrieval software. The final course project can be done in three different formats: (1) a programming project implementing a challenging and novel information retrieval application, (2) an extensive survey-style research paper providing an exhaustive look at an area of IR, or (3) a SIGIR-style experimental IR paper.

Complex Human Networks Reading Group Set up by Sandy Petland, Brian Clarkson and Tanzeem Choudhury at MIT.

Course Description: This is a reading group that was set up in order to explore the methods used in understanding structural and behavioral properties of large complex interactive networks. The initial focus was on the interactions of large groups of people.

Syllabus

Intended Audience: This class is open to anyone who wants to join. There is an email list that one has to sign up to in order to join. Although not explicitly stated on the website, it appears that the class is meant for an older audience, perhaps not undergraduates (the website has an alumni address).

Prerequisites: There are no prerequisites.

Textbooks and Articles: There is an extensive reading lists although it does not appear that all of the listed texts were actually used.

Assignments: This course was put together in order to exchange ideas. The only assignment is to come to each meeting having done the reading in order to facilitate discussion.

Recommender Systems By Naren Ramakrishnan at Virginia Tech.

Course Description: This is a computer science course that looks at recommender systems. The topics to be covered include the history, motivation, and overview of recommender systems; Models of Recommender Systems; search engines; algorithmics; the small world phenomenon; integrated approaches and Commercial and Broadening Aspects of Personalization.

Syllabus

Intended Audience: This class is for computer science graduate students only.

Prerequisites: Graduate Student Standing. One or more of CS5114 (algorithms), CS5485 (numerical analysis), CS5604 (information retrieval), CS5614 (database systems), CS5724 (HCI), and CS 5714 (usability engineering). It is expected that students have a primary motivation to study recommender systems, as (i) a field in its own right, (ii) a methodology to personalize information in a domain of interest, and/or (iii) an excellent example of integrated and amalgamated research in computer science.

Textbooks and Articles: There is no set text for this class, only readings from current literature, available either on the web or via handouts (there were no links or texts given on the website).

Assignments:The course will be centered around presentations and discussions led by the instructor, with a semester-long student project. The instructor will attempt to obtain industry involvement for most student projects (already two companies have agreed to participate by providing access to data and other resources). There will be *no* homeworks/assignments. Student participation in discussions is a must and will constitute 25% of the grade. The remaining 75% of the grade will go to your project. Students are encouraged to work in groups of 2-3, depending on the size of the project. Every project is expected to lead to a quality publication, in a premier conference, or a journal.

Social Network Analysis By Steve Borgatti at the University of Essex.

Course Description: This is a two-week summer school course that looks at the analysis of social networks. The topics covered by this class are as follows: the mathematical foundations of social networks, social network data, visualization of network data, cohesive subgroups, brokerage and ego networks, centrality and centralization, structural equivalence and Block Modeling and how to test a hypothesis in this field.

Syllabus

Intended Audience: The audience for this class appears to be students interested in information science. The University of Essex seems to be a specialized summer program with all classes in this area.

Prerequisites: There are no specifically stated prerequisites, but a mathematical/computational background seems necesary to gain entry to this summer program.

Textbooks and Articles: There are assigned and optional readings for every day, but no projects/presentations associated with the readings.

Assignments: There are labs during most class meetings, but there do not seem to be any grades/tests. This class seems more informational than part of a real academic curriculum.

Create Engaging Web Applications Using Metrics and Learning on Facebook by BJ Fogg and Dave McClure at Stanford University Dept. of Computer Science.

Course Description: This is an experimental course (being taught for the first time in fall 2007) where the aim is to have students learn to create, launch and optimize web applications. Students will work in small groups to develop new applications for Facebook. This course will focus on how metrics and user feedback can help developers and product managers improve their applications.

Syllabus

Intended Audience: This class is intended for upper level computer science majors with programming knowledge. It is also highly experimental and requires students willing to take risks and explore new challenges.

Prerequisites: Knowledge of programming seems essential.

Textbooks and Articles: There will be some assigned readings to compliment the projects, but the main focus on this class is the development of new technology.

Assignments: This course includes three types of projects.

Computer Networks by Sugih Jamin at the University of Michigan.

Course Description: This course attempts to demonstrate how networks operate and how network applications are written. The course looks at the workings of the Internet and Ethernet and problems of transmission and congestion. The course involves heavy programming and some code writing.

Syllabus

Intended Audience: This class is only intended for upper level students with a background in computer science (specifically programming) and mathematics.

Prerequisites: One should know what processes and threads are and be familiar with concurrency and interprocess communication. EECS 482 (Introduction to Operating Systems) is a strict pre-requisite. One must also have good working knowledge of C and UNIX. An introduction to probability course such as EECS 401, EECS 501, Math 425, Math 525, or Stat 412 is highly recommended as a co-requisite.

Textbook and Articles: This course uses two textbooks.

Assignments: 1 Final Exam: 20%, 2 Take-home, open book, open note Exams: 30% (15% each), 1 Course Project: 48%, Class Participation: 2%

Information Retrieval, Discovery and Delivery By Andrea LaPaugh at Princeton University

Course Description: This course examines the methods used to search for information in large digital collections (e.g. Google) and how digital content is gathered by search engines. It involves the study of classic techniques of indexing documents and searching text and also new algorithms that exploit properties of the Web (e.g. links) and other digital collections, including multimedia collections. Techniques include those for relevance and ranking of document, exploiting user history, and information clustering. It also examines systems aspects of search technology: how distributed computing and storage are used to make information delivery efficient.

Syllabus

Intended Audience: This is an upper level computer science class, not intended for a general audience.

Prerequisites: COS 226 (lower-level CS courses).

Textbooks and Articles: This class has one assigned text as well as some supplementary reading listed. Assigned articles are marked on the syllabus.

Assignments: Problem sets 25%, Two exams 30% (15% each), Class presentation 10%, Implementation Project 35%.

Scaling, Power Laws and Small World Phenomena in Networks By Don Towsley at the University of Massachusetts

Course Description: This seminar explores different manifestations of scaling phenomena and power laws. Not only do they arise in the context of network traffic characterization, but also in the description of network topologies. This will lead naturally to the “small world” phenomena that has so recently been in the press. The first third or so of the semester focuses on scaling in traffic models. It then looks at a very interesting set of papers on control mechanisms that produce power laws. One consequence of the theory in these papers is an explanation of the multiscalar traffic behavior. The remainder of the course will cover power laws in network topologies, culminating in the small world phenomena. we will explore what it is how it is poorly explained by traditional graph models, and examine preliminary models explaining the phenomena. The focus of the final weeks shifts to an entirely different topic, namely how to model networks as nonlinear dynamical systems.

Syllabus

Intended Audience: This class is intended for a more advanced computer science audience.

Prerequisites: A course on networking, a course on probability theory, and reasonable mathematical maturity.

Textbooks and Articles: There are assigned articles for every topic linked on the reading list on the course webpage.

Assignments: This course can be taken for one credit or three credits. For one credit, students are expected to attend the course and present one paper. For three credits, students are expected to either turn in detailed critical summaries of all of the papers or to propose and complete a course project.

Information Retrieval By Javed Aslam at Northeastern University.

Course Description: This is an undergraduate course on information retrieval. This course serves as an introduction to information retrieval systems and different approaches to information retrieval. Topics covered include evaluation of information retrieval systems; retrieval, language, and indexing models; file organization; compression; relevance feedback; clustering; distributed retrieval and metasearch; probabilistic approaches to information retrieval; web retrieval; filtering, collaborative filtering, and recommendation systems; cross-language IR; and machine learning for information retrieval.

Syllabus

Intended Audience: This is a class about information science which appears to be open to all undergraduates.

Prerequisites: There are no listed prerequisites and the class does not seem heavily focused on mathematics or programming.

Textbooks and Articles: There is one required text for this class: Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto. The book Managing Gigabytes, 2nd edition, by Witten, Moffat, and Bell is optional reading.

Assignments: All of the assignments are lain out here. Homeworks 30%, Projects 40%, Exam: 30%.

Networks By Jon Kleinberg and David Easley at Cornell University.

Course Description: A course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. Topics include: how opinions, fads, and political movements spread through society; the robustness and fragility of food webs and financial markets; and the technology, economics, and politics of Web information and on-line communities.

Syllabus

Intended Audience: This course is open to all undergraduates (it is cross-listed under economics, sociology and information science).

Prerequisites: Almost no knowledge of specific mathematical content is assumed, other than some basic probability (random variables, expectation, independence, and conditional probability), which we will briefly review early in the course. However, the main goal of the course will be to build mathematical models of phenomena such as we see in the Gladwell and Schelling books. As such, students will be expected to interpret and work with mathematical models as they come up the course; at the same time, students should also think about how to relate these models to phenomena at a qualitative level.

Textbooks and Articles: There are two assigned books for this course. There are also supplemental readings linked through the course webpage.

Assignments: There is a Midterm and Final Exam, approximately 6 problem sets and a short paper due the last week of class. The paper is designed to be an exploration of a topic related to the course, containing both a discussion of prior work, and some novel discussion or analysis of the topic. Class blog: As discussed above, there is a class weblog and each student should make at least three posts to it as part of the graded coursework. See the accompanying handout describing the format and schedule for blog posts. Grades on homework,the paper, blog posts, the midterm, and the final will be weighted as follows: Midterm: 20%, Final: 30%, Homework: 20%, Short Paper: 20%, Blog Posts: 10%.

Networks: Theory and Applications by Professor Lada Adamic, Ph.D at the University of Michigan

Course Description: The course covers topics in network analysis, from social networks to applications in information networks such as the Internet. The class will involve introducing basic concepts in network theory, discussing metrics and models, using software analysis tools to experiment with a wide variety of real-world network data, and studying applications to areas such as information retrieval. Labs are an integral part of the course as a final group project, in which students take the concepts they learned and apply them to networks that they select.

Syllabus

Intended Audience: This course is taught to master’s students at the University of Michigan School of Information.

Prerequisites: There appears to be no prerequisites for this graduate course.

Textbooks and Articles: There are two required texts for this course and several assigned supplemental articles are listed on the course page here

Assignments: A grading weight distribution is not listed but coursework consists of labs, problem sets, a midterm exam, and a final project.

Demos: A set of course demos can be found here.

Computer Supported Cooperative Work: Analyzing and Designing Online Communities by Professor Robert Kraut at Carnegie Mellon

Course Description: This research-oriented seminar is intended to help students analyze communities, to understand what makes them succeed or fail, with an eye toward designing and improving them. The course will cover such types of communities as open-source software development projects, Wikipedia, health support groups, and massively multi-player games. It will deal with such conceptual issues as the basis of commitment to groups, free riding and other motivational problems, communication, coordination, control and recruitment, socialization and retention.

Intended Audience: This course is intended for computer science students as well as behavioral and social science students.

Prerequisites: There are no stated prerequisites for this class.

Textbooks and Articles: A list of readings for each week can be found on the course page.

Assignments: The range of assignments seem to include a weekly online discussion, a midterm project, and a final project as well as a few other homeworks.

TeleConferencing and Computer-Supported Cooperative Work by Professor Cliff Lampe at Michigan State University

Course Description: This class will focus on combing hands-on experiences with communication technologies with research on how work can be facilitated through the use of ICT. Email, IRC, chat, texting, groupware, teleconferencing, collaborative virtual environments, wikis, blogs and more will be technologies students will experience and conduct work through. Each class will mix readings and group work. While some lecturing will take place, for the most part this class will involve hands-on, practical experiences with telecommunication technologies.

Intended Audience: This is an upper level course intended for undergraduates.

Prerequisites: Prerequisites are not clearly stated, but similar classes require students to have taken a number of introductory and intermediate courses.

Textbooks and Articles: There are not textbooks listed for this course. A number of readings appear on the course page.

Assignments: Participation and attendance (10%), Article summaries (20%), Individual assignments (30%), Final group project (40%).