Tik-122.101 Special Course in Information Technology
Graphical Models - course description
Graphical models are probabilistic models, which have origins in many different research communities, such as artificial intelligence, statistics and neural networks. The general framework of graphical models provides a mathematical formalism, that helps in understanding similarities and differences between various learning architectures and algorithms. Typically, algorithms in a particular network architecture can be derived from the inference and learning machinery of general graphical models. On such example is the junction tree algorithm, which can be used to derive inference rules for the Gaussian mixture models and hidden Markov models, for instance. The course will cover topics around Bayesian networks, Boltzmann machines, mean-field and variational approximations to learning, latent variable models and propagation of probabilities in networks with loops. The material is based on a recent textbook (see above), which is a collection of original contributions of leading researchers in the field.
The course is suitable for advanced graduate students and students with a specific research focus on graphical models. The course has great thematic overlap with the course Inference and Learning in Bayesian Networks organized last year (in Finnish), so think carefully before enrolling. Attendance will be limited to 15-20 students.
To pass the course, students are expected to give an oral presentation on one of the given topics (chapters) and hand out a summary to others. In addition to active seminar participation, there will be exercises and a programming exercise. Exercises must be returned by January, 31st, 2003.
The course information is presently missing from Topi. You can enroll by by sending e-mail to the course assistant and by attending the first seminar on September 18th.
Zoubin Ghahramani and Sam Roweis: Probabilistic Models for Unsupervised Learning, tutorial at 1999 Neural Information Processing Systems (NIPS'99).
Max Welling and Geoffrey E. Hinton. A New Learning Algorithm for Mean Feld Bolrzmann Machines. In Proceedings of the 12th International Conference on Artificial Neural Networks, pp.351-357, 2002.
Cecil Huang and Adnan Darwiche. Inference in Belief Networks: A Procedural Guide. International Journal of Approximate Reasoning, 15 no 3, pp 225-263, 1996.
The practical assignment consists of two parts. In the first part, there are (simple) questions related to the topics covered during the course ps, pdf. In the second part you are asked to construct a program of your own ps, pdf. The exercises are to be completed before the 31st of January 2003. They should be handed in to the course assistant (room C311 in the computer science building).
Wednesday, 06-Nov-2002 13:21:44 EET