New Course. Take the course flyer! 
Description:
Introduction to fundamental ideas and techniques of
statistical modeling, with an emphasis on conceptual understanding and
on the analysis of real data sets.
Assignments will involve some
programming.
Prerequisite: - Lower division
introductory course in statistics and probability at the level of Ma
2, linear algebra, or permission of
instructor.
Syllabus:
- Simple and multiple linear regression: estimation,
inference, assessing the fit, model checking.
- Singular value decomposition (SVD) and regularization.
- Principal components, Gaussian processes and
Karhunen Loeve decomposition.
- Cross validation.
- Resampling methods and the bootstrap.
- Topics in learning and classification.
Textbooks:
- Weisberg, "Applied Linear Regression" Wiley.
(required)
- Hansen, "Rank-Deficient and Discrete Ill-Posed
Problems: Numerical Aspects of Linear Inversion"
SIAM. (optional)
- Efron and Tibshirani, "An Introduction to the
Bootstrap" Chapman and Hall. (optional)
- Venables and Ripley, "Modern Applied Statistics
with S-Plus" Springer. (optional)
Handouts:
All handouts given in class will be stored in a binder
in Maria's office or posted online. Maria's office: 307
Firestone. You can contact Maria at any time (between
8:30am and 5pm) if you need administrative
information. Her phone number is 626-395-4555.
Teaching
Assistant and Office Hours:
Yajun Mei,
myajun@its.caltech.edu
Monday 4--6pm and by appointment, 382 Sloan
Grading:
Homework assignments: 60%
Homework will generally be distributed on Wednesdays
and due in class the following Wednesday.
There will be about 6 or 7 assignments, and your
lowest score will be dropped in the final grade.
Late homeworks will NOT be accepted for grading
(medical emergencies excepted with proof).
Final exam: 40%. There will be a
take-home final exam
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