Course Workload
The course consists of lectures, readings, homework assignments, and a final project. Computers generate vast amounts of data from a wide variety of sources (experiments, simulations, ...). The purpose of this course is to learn the best and most general approaches to get the most out of data. The course will explore both theoretical foundations and practical applications. Students will be analyzing several kinds of data, including the ones they syntesize, or simulate.
Textbook
No textbook is selected which fits the material in this course. Instead, students will be asked to attend the lectures, take notes, and work on the lectures which will be posted on the course website. Additional papers and book chapters will also be provided as useful reading material, like in:
Numerical Recipes 3rd edition Chapters 7, 10, 14, 15
Very useful site for probablity theory
Computer age statistical inference
The Jackknife, the Bootstrap and Other Resampling Plans
Homework assignments
Four HW assignments are planned approximately every two weeks; these will be a mix of analytic and programming based problems. Some graduate level problems will be more challenging. Homeworks count for 50% of your grade. There will be a Midterm project with additional 20% and a Final project with 30%.Late days
All assignments are due at Midnight on the due date. Each student will be allotted six free days which can be used to turn in homework assignments late without penalty. When free days are used up, late homeworks will be penalized 10% per day. Homeworks will not be accepted more than seven days past the deadline. Exceptions might be made for serious illness or other emergency.Grade policy:
Lecture attendance required not to risk upto 20% of the total grade points
HW Assignments (50%)
Midterm (20%)
Final project (30%).