Index of /courses/cse446/26sp/schedule/lectures

[ICO]NameLast modifiedSizeDescription
[PARENTDIR]Parent Directory  -  
[ ]01-intro, probability review.pdf2026-06-10 09:36 57M 
[ ]02-maximum likelihood estimation.pdf2026-06-10 09:36 16M 
[TXT]03-diabetes.txt2026-06-10 09:36 64K 
[ ]03-linear regression.pdf2026-06-10 09:36 4.4M 
[ ]04-linear regression w basis funcs.pdf2026-06-10 09:36 374K 
[ ]05-train test split cross validation.pdf2026-06-10 09:36 1.6M 
[ ]06-bias variance tradeoff.pdf2026-06-10 09:36 5.8M 
[ ]07-regularization.pdf2026-06-10 09:36 7.4M 
[ ]08-sparsity and lasso.pdf2026-06-10 09:36 6.6M 
[ ]09-gradient descent.pdf2026-06-10 09:36 4.5M 
[ ]10-convexity.pdf2026-06-10 09:36 690K 
[ ]11-gradient descent theory.pdf2026-06-10 09:36 329K 
[ ]12-prediction pitfalls.pdf2026-06-10 09:36 103M 
[ ]13-classification and logistic regression.pdf2026-06-10 09:36 9.2M 
[ ]14-nearest neighbours.pdf2026-06-10 09:36 7.8M 
[ ]15-trees.pdf2026-06-10 09:36 2.2M 
[ ]16-bootstrap.pdf2026-06-10 09:36 1.7M 
[ ]17-random forests.pdf2026-06-10 09:36 2.9M 
[ ]18-kernels.pdf2026-06-10 09:36 2.6M 
[ ]19-neural nets.pdf2026-06-10 09:36 11M 
[ ]20-conv nets.pdf2026-06-10 09:36 11M 
[ ]21-rnns.pdf2026-06-10 09:36 2.2M 
[ ]22-attention.pdf2026-06-10 09:36 2.7M 
[ ]23-transformers.pdf2026-06-10 09:36 5.8M 
[ ]24-K-means.pdf2026-06-10 09:36 705K 
[ ]25-GMM.pdf2026-06-10 09:36 1.2M 
[ ]26-PCA.pdf2026-06-10 09:36 10M 
[ ]27-SVD-annotated.pdf2026-06-10 09:36 5.8M 
[TXT]house_train_kaggle.csv2026-06-10 09:36 459K 
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