Prerequisites: CS312, Recommended: CS470
Goals: Introduce and study the philosophy, utility, and models of machine learning, such that students are able to propose original research with potential follow-up in a graduate research program. In particular, symolic (artificial intelligence) and other non-neural network models of inductive learning will be emphasized. The student will also learn research skills applicable to other fields of computer science.
Text: Tom Mitchell, Machine Learning. There is also a packet including a copy of the overheads to be used during class. You will be expected to read the assigned literature before and typically after the scheduled lecture. To help motivate reading I will pass around a sheet for you to mark whether you have done a complete and careful reading, a partial reading, or no reading. A total reading counts for 2 points, partial - 1 point, none - 0 points. Each day mark if you have done the reading for the lecture to be given that day. The grading will be non-linear such that missing one or two readings does not hurt much, but it picks up fast after that. Errata and more information about the text can be found at the author's site.
Homework: There will be homework given with each of the models. Homework is due at the beginning of the first class period after the homework is assigned.
Simulations: There will be in-depth simulations and analysis of three current learning models. Software for these simulations will be made available and simulations can be done in the open labs or on the machine of your choice. Information for each of the simluations can be found here.
Late assignments: Assignments are expected on time (beginning of class on due date). Late papers will be marked off at 5%/school day late including the due date. However, if you have any unusual circumstances (sick, out of town, unique from what all the other students have, etc.), which you inform me of, then I will not take off any late points. Nothing will be accepted after the last day this class is held.
Grading (~): Reading: 10%, Simulations and Homework: 25%, Midterm: 21%, Project: 22%, Final: 22%. Grading is on a curve and some amount of subjectivity is allowed for attendance, participation, perceived effort, etc. If you think, you'll be all right.
Project: An in-depth effort on a particular aspect of machine learning. A relatively extensive literature search in the area is expected with a subsequent bibliography. Good projects are typically as follows: Best: Some of your own original thinking and proposal of a learning model, paradigm, system, etc. This (and other projects) are typically well benefited by some computer simulation to bear out potential. Very Good: Starting from an in-depth study of some current model, strive to extend it through some new mechanisms. Not Bad: A study of a current model with an in-depth analysis of its strengths, weaknesses, potential, and suggested research. Not Good: A description of a current model. The earlier you start the better. Note that in a semester course like this, you will have to choose a topic when we have only covered half of the material. That does not mean your project must cover items related to the first half of the semester. You should use your own initiative and the resources available (library literature, texts, me, etc.) to peruse and find any topic of interest to you, regardless of whether we have or will cover it in class. There are many interesting models which we will not have time to cover. I have papers in my office which can be looked over and copied under constraint of the 15 minute rule. I can also send for most any paper you wish through interlibrary loan, (and will do so), but it usually takes 2-3 weeks
If you have a question or concern regarding class topics or the grading of your homework or examinations, first talk with the TA. If your concern is about grading take a moment to think out the problem first and have a good argument for your appeal. If you still feel that justice has not been done after working with the TA, come see me.
ENJOY!