Letter Grade | Lowest Score |
A+ | 96.5 |
A | 93.5 |
A- | 90.0 |
B+ | 86.5 |
B | 83.5 |
B- | 80.0 |
C | 70.0 |
F | 0.0 |
Date | Reading (before class) | Homework | Topic/Content | Presentation |
1/9/24 | none | none | Syllabus, introduction | Intro.pdf |
1/11/24 | HTF Ch. 1 and Ch. 2.1, 2.2, and 2.3 | Homework 1 | Least-squares, nearest-neighbors | lecture-1.pdf mixture-data-lin-knn.R ESL.mixture.rda |
1/16/24 | HTF Ch. 2.4 | none | Class cancelled due to poor weather conditions | |
1/18/24 | HTF Ch. 2.4 | none | Decision theory | lecture-2.pdf |
1/23/24 | none | none | Loss functions in practice | lecture-2a.pdf prostate-data-lin.R prostate.csv |
1/25/24 | HTF Ch. 2.7, 2.8, and 2.9 | Homework 2 | Structured regression | lecture-3.pdf ex-1.R ex-2.R ex-3.R |
1/30/24 | HTF Ch. 3.1, 3.2, 3.3, 3.4 | none | Linear methods, subset selection, ridge, and lasso | lecture-4a.pdf linear-regression-examples.R lecture-5.pdf lasso-example.R |
2/1/24 | none | none | Linear methods, subset selection, ridge, and lasso (cont.) | lecture-5.pdf lasso-example.R Suggested supplemental reading: HTF Ch. 3.6, 3.7, 3.8, and 3.9. Suggested supplemental exercises: Ex. 3.12, 3.18 |
2/6/24 | HTF Ch. 3.5 and 3.6 | none | Linear methods: principal components regression | lecture-6.pdf pca-regression-example.R |
2/8/24 | HTF Ch. 4.1, 4.2, and 4.3 | none | Linear methods: Linear discriminant analysis | lecture-8.pdf simple-LDA-3D.R |
2/13/24 | HTF Ch. 5.1 and 5.2 | Homework 3 | Basis expansions: piecewise polynomials & splines | lecture-11.pdf splines-example.R mixture-data-complete.R |
2/15/24 | HTF Ch. 6.1-6.5 | none | Kernel methods | lecture-13.pdf mixture-data-knn-local-kde.R kernel-methods-examples-mcycle.R |
2/20/24 | HTF Ch. 7.1, 7.2, 7.3, 7.4 | none | Model assessment: Cp, AIC, BIC | lecture-14.pdf effective-df-aic-bic-mcycle.R |
2/22/24 | HTF Ch. 7.10 | none | Cross validation | lecture-15.pdf kNN-CV.R Income2.csv |
2/27/24 | HTF Ch. 9.2 | Homework 4 | Classification and Regression Trees | lecture-21.pdf mixture-data-rpart.R |
2/29/24 | HTF Ch. 8.7, 8.8, 8.9 | none | Bagging | lecture-18.pdf mixture-data-rpart-bagging.R nonlinear-bagging.html |
3/5/24 | HTF Ch. 11.1, 11.2, 11.3, 11.4, 11.5 | none | Introduction to Neural networks | lecture-31.pdf nnet.R |
3/7/24 | HTF Ch. 11.1, 11.2, 11.3, 11.4, 11.5 | none | Introduction to Neural networks (cont.) | lecture-31.pdf nnet.R |
3/19/24 | HTF Ch. 11.1, 11.2, 11.3, 11.4, 11.5 | none | Introduction to Neural networks (cont.) | lecture-31.pdf nnet.R |
3/21/24 | HTF Ch. 15.1, 15.2 | none | Random Forest (distribute midterm) | lecture-25.pdf random-forest-example.R |
3/26/24 | HTF Ch. 10.1 | none | Boosting and AdaBoost.M1 | lecture-22.pdf boosting-trees.R |
3/28/24 | HTF Ch. 10.2-10.9 | none | Boosting and AdaBoost.M1 (part 2) | lecture-23.pdf |
4/2/24 | HTF Ch. 10.10, 10.13 | none | Boosting and AdaBoost.M1 (part 3) | lecture-24.pdf gradient-boosting-example.R |
4/4/24 | HTF Ch. 14.5 | none | Principal curves and surfaces | lecture-28.pdf principal-curves.R |
4/9/24 | HTF 14.8 | none | Multidimensional scaling | lecture-30.pdf MDS-examples.R |
4/11/24 | HTF 14.5.3 | none | k-means, hierarchical, and spectral clustering | lecture-29.pdf spectral-clustering.R |
4/16/24 | none | none | Clustering with mixtures | lecture-32.pdf normal-mixture-examples.R |
Make sure to list the code, output, and any plots in your repo so that readers can understand everything you did.
Resubmissions are only allowed if the initial submission was made on time. A resubmission is due within one week of receiving feedback from TA and there is a maximum of 2 resubmissions for each assignment.
Using the RMarkdown/Jupyter notebook and Github mechanism, implement the following tasks:
lpsa
as the outcome, and use all other variables in the data set as predictors.
glmnet
in R or sklearn.linear_model
and its alpha
argument in Python) and tune the value of lambda
, i.e., for a sequence of lambda
find the value of lambda
that approximately minimizes the test error.
lambda
.
Goal: Understand the overall supervised learning process and test error.
Use the training and testing zip code data to develop a k-nearest neighbor (k-NN) model to classify zip code digit [0-9] based on a 16x16 scanned greyscale image of the digit: Info, Training, Testing