STAT 535 Handout 0

S T A T 5 3 5 S t a t i s t i c a l L e a r n i n g

Handout 0 - About the course

Lecture place and time: Tuesdays & Thursdays 11:30 - 12:50 in Mary Gates Hall 248

Course web site: http://www.stat.washington.edu/courses/stat535/fall11 Use it!

Instructor: Marina Meila

mmp@stat.washington.edu

Office hours: Monday 2-3pm in PDL B-321
Class mailing list: stat535a_au11@uw.edu

Textbooks: Optional textbook: Koller and Friedman "Graphical Probability Models". We will occasionally use chapters from Jordan's "Introduction to Probabilistic Graphical Models" and other reading materials.
Format: The course will consist of two weekly lectures and a series of homework assignments. Active class participation by questions, answers and comments is important and therefore it will be part of your grade.
Assignments: Typically assignments will be handed out on Tuesdays and will be due the next Tuesdays before class. The assignments will consist of (1) short programming assignments (typically, to implement a version or a special case of an algorithm presented in class) to be done in the programming language of your choice and (2) algorithm problems or other questions related to the graphical models we will be studying. The programming assignments will be split into two parts: output and results, which should be handed in on paper, and that will be graded, and the code part, which is submitted electronically and is typically not graded. Late homeworks will only be accepted in exceptional circumstances.
Teamwork: Each class participant submits her/his homework individually. Unless explicitly allowed to do so, you are required to write your own code. Downloading code from the web is not allowed. Excepted from this rule is helper software, like plotting and displaying algorithms.
Exams: There will be a final exam. To be decided about Midterms.
Grading: (Approximately) 10% class participation, 70% assignments, 20% final exam.
Prerequisites: Basic experience with a high level programming language like C, C++, Java, Pascal, Lisp, Matlab, R, Splus. Basic probability and calculus. Fundamentals of algorithms and data structures at the level taught in STAT 534.
Last modified: Fri Sep 30 18:22:42 PDT 2011

Instructor:	Marina Meila
	mmp@stat.washington.edu
Office hours:	Monday 2-3pm in PDL B-321
Class mailing list:	stat535a_au11@uw.edu
Textbooks:	Optional textbook: Koller and Friedman "Graphical Probability Models". We will occasionally use chapters from Jordan's "Introduction to Probabilistic Graphical Models" and other reading materials.
Format:	The course will consist of two weekly lectures and a series of homework assignments. Active class participation by questions, answers and comments is important and therefore it will be part of your grade.
Assignments:	Typically assignments will be handed out on Tuesdays and will be due the next Tuesdays before class. The assignments will consist of (1) short programming assignments (typically, to implement a version or a special case of an algorithm presented in class) to be done in the programming language of your choice and (2) algorithm problems or other questions related to the graphical models we will be studying. The programming assignments will be split into two parts: output and results, which should be handed in on paper, and that will be graded, and the code part, which is submitted electronically and is typically not graded. Late homeworks will only be accepted in exceptional circumstances.
Teamwork:	Each class participant submits her/his homework individually. Unless explicitly allowed to do so, you are required to write your own code. Downloading code from the web is not allowed. Excepted from this rule is helper software, like plotting and displaying algorithms.
Exams:	There will be a final exam. To be decided about Midterms.
Grading:	(Approximately) 10% class participation, 70% assignments, 20% final exam.
Prerequisites:	Basic experience with a high level programming language like C, C++, Java, Pascal, Lisp, Matlab, R, Splus. Basic probability and calculus. Fundamentals of algorithms and data structures at the level taught in STAT 534.