Instructor: Todd Kuffner  (kuffner  who is  @  wustl  *dot*  edu )

Lecture: 8:30 - 10:00, Tuesday/Thursday, Location: Cupples I, Room 215

Office Hours: Tuesday 11:00 - 12:00 and Thursday 10:00 - 11:00

Final Exam Date: May 4, 2018, 1:00 - 3:00 pm

Course Description: A modern course in multivariate statistics. Elements of classical multivariate analysis as needed, including multivariate normal and Wishart distributions. Clustering; principal component analysis. Model selection and evaluation; prediction error; variable selection; stepwise regression; regularized regression. Cross-validation. Classification; linear discriminant analysis. Tree-based methods. Time permitting, optional topics may include nonparametric density estimation, multivariate regression, support vector machines, and random forests.

Prerequisite: Multivariable calculus (Math 233), linear or matrix algebra (Math 429 or Math 309), multivariable-calculus-based probability and mathematical statistics (Math 493, Math 494) and linear models (Math 439). Prior knowledge of R at the level introduced in Math 439 is assumed.

Textbook:  An Introduction to Statistical Learning: with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
This textbook is required for many of the topics in the course. For some additional material in the course, the lectures are the primary reference, but freely-available references may also be suggested through Blackboard.

Homework: There will be homework assignments which will consist of mathematical statistics exercises and also R-based exercises.

Blackboard: During the semester, homework assignments, homework and midterm exam grades and any other course-related announcements will be posted to Blackboard or sent by email using Blackboard.

Attendance: Attendance is required for all lectures. The student who misses a lecture is responsible for any assignments and/or announcements made.

Grades:  The grade for the course will be based on Homework (20%), Exam I (20%), Exam 2 (20%) and the Final Exam (40%).

Final Course Grade: The letter grades for the course will be determined according to the following numerical grades on a 0-100 scale.

A+	impress me	B+	[87, 90)	C+	[77, 80)	D+	[67, 70)	F	[0,60)
A	93+	B	[83, 87)	C	[73, 77)	D	[63, 67)
A-	[90, 93)	B-	[80, 83)	C-	[70, 73)	D-	[60, 63)

Other Course Policies: Students are encouraged to look at the Faculty of Arts & Sciences policies.

• Academic integrity: Students are expected to adhere to the University's policy on academic integrity. 
• Auditing: There is an option to audit, but this still involves enrolling in the course. See the Faculty of Arts & Sciences policy on auditing. Auditing students will still be expected to attend all lectures and compete all required coursework and exams. A course grade of 75 is required for a successful audit.
  
• Collaboration: Students are encouraged to discuss homework with one another, but each student must submit separate solutions, and these must be the original work of the student. 
• Exam conflicts: Read the University policy. The exam dates for this course are posted before the semester begins, and thus you are expected to be present at all exams.
  
• Late homework: Only by prior arrangement. If a valid reason for an exception is not presented at least 36 hours before a homework due date, then it will not be accepted late (a zero will be given for that assignment).
  
• Missed exams: There are no make-up exams. For valid excused absences with midterm exams - such as medical, family, transportation and weather-related emergencies - the contribution of that midterm to the final course grade will be redistributed equally to the other midterm exam and final exam. Students missing both midterm exams and/or the final exam cannot earn a passing grade for the course.