Slides, notes, demos, and videos for lectures will be posted here.
Slides and demos will be updated immediately before lecture;
later slides correspond to material from previous years.
Topics may change based on student interest.
Lecture 1: Introduction
Lecture 2: Exploratory data analysis
Lecture 3: The perceptron
slides from 2021
video from 2020
apologies that I did not record this lecture this year! the content from 2020 is the same, but please review the slides to see the correct announcements
for async participation: please answer the questions from the 2020 video
Lecture 4: Finish EDA, start linear regression
Lecture 5: linear regression (Gradient descent)
Lecture 6: linear regression (SGD and QR)
Lecture 7: feature engineering (numeric features)
Lecture 8: feature engineering (boolean, nominal, ordinal features)
Lecture 9: feature engineering (demo, deep learning)
Lecture 10: train, test, validate
Lecture 11: generalization
Lecture 12: underdetermined least squares
Lecture 13: quadratic regularization
Lecture 14: bootstrap and bias variance tradeoff
Lecture 15: trees
Lecture 16: forests, start regularization
Lecture 17: regularization, ControlBurn
Lecture 18: finish ControlBurn, more regularization
Lecture 19: Loss functions
Lecture 20: Loss functions (classification and multi-class classification)
Lecture 21: Unsupervised learning and PCA
Lecture 22: Gaussian copula for missing value imputation
Lecture 21: Generalized low rank models
Lecture 21: Automated machine learning
Lecture 22: Fairness in machine learning
Lecture 23: Limitations and Dangers of Predictive Analytics
Lecture 23: Neural Networks
Lecture 24: Review