- Bootstrap methods for independent variables
- Consistency- sample mean
- Second order correctness - sample mean
- Heavy tailed random variables
- Results on sample quantiles
- Bootstrap confidence intervals
- Bootstrap methods for dependent variables
- Block bootstrap methods
- Second order correctness
- Selection of block size
- Other boostrap methods
- Elements of Machine learning
- Preliminaries
- Bagging, Subagging, and their properties
- Boosting and its variants and their properties
- Other machine learning algorithms (time permitting)
-
COURSE GRADE:
The grade for this course will be determined by
performance on homework assignments (10%), one midterm
exam (50%), and a project (40%).
IMPORTANT DATES for the HWs, Midterm and
the project work, etc.
-
Homework 1: Tu 9/6 (Due 9/20)
- Individual/group meetings
for project topics (9/13, 9/15)
- Homework 2: Th 9/22 (Due 10/06)
- One page project description due on
Th 9/29
- Homework 3: Tu 10/11 (Due 10/25)
- No class: Th 10/27
- Homework 4: Tu 11/01 (Due 11/29)
- One page updates on projects - due on
Tu 11/08
- Midterm Exam Wed 11/14 (Evening)
(Tentative)
- Presentation + final project report
(12/06, 12/08)