Instructor: Marek J. Druzdzel
"Science is not science fiction. It accepts the tests of observation and experiment, acknowledges the supremacy of fact over wish or hope. The smallest experiment can crash to earth the most attractive theory." --- Herbert A. Simon
INFSCI 2040 is an introductory course in research design for students in information science and related disciplines, such as computer science and intelligent systems. It is not designed to teach you the formal details of statistical procedures or to make you an expert practitioner of the specific design tools. That you can and should get in other specialized courses. The point of this class is to develop broad critical abilities and its emphasis is on the basic process of scientific inquiry. It aims at improving your ability to think about and frame research questions and to choose the ways to address them by empirical studies.
The approach taken in this course is somewhat unorthodox compared to all existing textbooks and courses on experimental design that I am aware of. We will start with the concept of causality and causal graphs and how they represent statistical independence. Causal graphs are close to directed probabilistic models, such as Bayesian networks, increasingly used in decision support systems. You will learn to reason about relevance and dependence. I believe this will help you in gaining insight into the structure of scientific experiments and in understanding what experimentation is about. Like any course on experimental design, this course will cover the basics of the design of experiments and topics that are directly related to it, such as identifying and articulating research problems, formulating testable hypotheses, measurement and data collection, subject-experimenter artifacts and their control, describing and displaying data, interpreting and drawing conclusions from data analysis, and reporting research findings and their implications. The course will also cover less orthodox topics such as: problems with the classical hypothesis testing, elements of Bayesian approaches to research design, and research methods used in information science and artificial intelligence, notably simulation and computer discovery.
The intended participants of this course are students who are interested in research and in pursuing academic careers. It is certainly a useful course for the doctoral students in the Department of Information Science and Telecommunications and the Intelligent Systems Program. I believe that it is also useful for those M.Sc. students who are working towards their Master's theses. It may be less useful and less interesting for those students who plan to pursue application-oriented careers.
As you might have already experienced as aspiring scientists, being a researcher requires intelligence, independent, creative thinking, and most of all commitment to hard working. This course reinforces this. There will a higher than usual amount of readings. I have selected them in such a way that they are fun to read and I expect that you will do them all with pleasure. You will be expected to prepare an abstract of one paper from among those on the list of readings and to present it in class. There will also be a term project that will involve writing a research proposal. This should be a real proposal. If you have not written your Ph.D. or M.Sc. proposal yet or if you would like to apply for funds for your studies, this is an excellent opportunity to do so. Students in this class have had an excellent track record in this respect (the total of well over $600,000 in research funding since fall 1994). Another experience that this course will provide you is peer review, subjecting your work to anonymous judgment of your colleagues and judging the work of others. The workload in this class will be heavy, but I believe that you will find it interesting and important. I require your commitment, doing the readings, coming to classes, and being their active participant. In return, I promise that you will have fun and you will learn many useful skills.
Syllabus (Fall 2011, PDF)
Marek Druzdzel's teaching page
Marek Druzdzel's home page