STATS 100C: Linear Models

Syllabus (Spring 2014)

Instructor: Ying Nian Wu (ywu@stat.ucla.edu)
TR 11:00AM-12:15PM FRANZ 1260
Office: 8971 Math Sciences Bldg, office hours: TR 2-2:50pm

Attendance (5%)

Weekly Homework (35%): No late homework

Midterm Exam (20%): Thursday, October 30, 2014, in class

Final Exam (40%): Friday, December 19, 2014, 3:00pm-5:00pm

Correlation, simple linear regression, least squares

Optimality of least squares, statistical inference

Multiple linear regression, matrix notation

*Regularization: ridge regression, Lasso

Logistic regression, maximum likelihood

*Supervised learning: adaboost, support vector machine

*Unsupervised learning: matrix factorization

*Neural network, deep learning
(* are advanced and modern topics)

Rawlings, J. O., Pantula, S. G., and Dickey, A. D. (1998) Applied Regression Analysis, A Research Tool. Springer.

Sheather, S. J. (2009) A Modern Approach to Regression with R. Springer.

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2014) An Introduction to Statistical Learning with Applications in R. Springer.

We will use R, a free open-source statistical software. You can download R at http://cran.stat.ucla.edu