CSE/STAT 416  Intro to Machine Learning
Sewoong Oh
University of Washington
TTh 10:0011:20, CSE2 G20
Regression
Lectures:
[0.Overview] [1.Regression annotated] [ML pipeline demo, and html]
Optional Readings: [ESL, Sections 1, 2.3.1]
[Overview slides from 2018Sp]
[Regression slides from 2018Sp]
[2.Validation annotated] [polynomial fit demo, and html]
Optional Readings: [ESL, Sections 2.3.1]
[Assessing performance slides from 2018Sp]
[Gradient descent slides]
[Gradient descent annotated slides]
[section slides by Joshua]
Optional Readings:
[ESL, Sections 4.5.1]
[3.Regularization annotated] [Ridge regression demo, and html]
Optional Readings:[ESL, Sections 3.13.2, 7.17.2]
[ESL, Sections 7.4, 3.4.1]
[Ridge regression slide rom 2018Sp]
[4.Nonquadratic regularizers annotated] [Lasso regression demo and html]
Optional Readings:
[ESL, Sections 2.9, 5.5.2, 7.2]
[ESL, Sections 3.4.2, 7.10]
[Lasso regression slides from 2018Sp]
Section 2. Thurs, April 11: Assignment 1 tips, Bias variance tradeoff.
You can view the notebooks by viewing the HTML version here.
To try out the demo yourself, download the ipynb and data files.
[BiasVariance demo (html  ipynb)]
[Pandas demo ipynb]
[Philadelphia_Crime_Rate data]
Optional Readings: [ESL, Section 7.3]
[5.Nonquadratic loss slides]
[5.Nonquadratic loss annotated slides]
[nonquadratic loss demo] and
[html]
Optional Readings: [ESL, Sections 3.4.13.4.3, 3.6]
Classification
Lectures:
[6.Classification slides],
[6.Classification annotated slides]
[Classification demo] and [html]
Optional Readings:
[ESL, Sections 1, 2.3.1, 4.14.2]]
[Classification slides from 2018Sp]
[7.Overconfident predictions slides]
Optional Readings:
[ESL, Sections 4.4.14.4.4, 9.1.2, 7.57.6]
[Logistic regression slides from 2018Sp]
[8.Decision tree slides]
Optional Readings:
[ESL, Sections 9.2.19.2.3]
[Decision tree slides form 2018Sp]
[Classification review slides]
Optional Readings:
[ESL, Sections 2.6, 8.2, 2.2, 9.2.4]
[9.Boosting slides]
Optional Readings:
[ESL, Sections 9.2.4, 10.110.10]
[Deriving AdaBoost formula]
[Explaining AdaBoost (Schapire 2013)]
[Boosting slides 1 from 2018Sp]
[Boosting slides 2 from 2018Sp]
[10.Precision and Recall slides]
[10.Precision and Recall annotated slides]
[Tree ensemble slides]
Optional Readings:
[ESL, Sections 11.9.1, 15.115.4]
