Slides, notes, demos, and videos for lectures will be posted here. Topics may change based on student interest.
9-3-2020 Introduction video
9-8-2020 Exploratory data analysis + finish introduction video
9-10-2020 Perceptron VOD (available later) Zoom recording (available earlier, but not in China)
9-15-2020 Finish EDA + start linear models VOD Zoom recording
9-17-2020 Linear models (gradient descent) VOD Zoom recording
9-22-2020 Linear models (parallel and stochastic) VOD Zoom recording
9-24-2020 Linear models (convexity, proofs) VOD Zoom recording
9-29-2020 Feature engineering (polynomials) VOD Zoom recording
10-1-2020 Feature engineering (boolean, ordinal, nominal, text) VOD Zoom recording
10-6-2020 Train test validate VOD Zoom recording
10-8-2020 Generalization VOD Zoom recording
10-13-2020 Underdetermined least squares VOD Zoom recording
10-15-2020 Quadratic regularization + bootstrap VOD Zoom recording
10-20-2020 Bias variance tradeoff VOD Zoom recording
10-22-2020 Real loss functions VOD Zoom recording
10-27-2020 Boolean loss functions VOD Zoom recording
10-29-2020 Multiclass and ordinal loss functions VOD Zoom recording
11-3-2020 Regularization VOD Zoom recording
11-5-2020 Limits VOD Zoom recording
11-10-2020 Unsupervised learning (PCA) VOD Zoom recording
11-12-2020 Generalized low rank models VOD Zoom recording
12-1-2020 Missing Data Imputation using Gaussian copula VOD
12-3-2020 AutoML VOD
12-8-2020 Rich Caruana: InterpretML: Explainable Boosting Machines (EBMs) VOD
12-10-2020 Alejandro Schuler: Clinical Trials and Digital Twins VOD
12-15-2020 Hamdan Azhar: Telling stories with data VOD
Introduction
Demo: SIR model
Exploratory data analysis
Demo: exploratory data analysis
Linear models
The perceptron algorithm
Linear regression
Demo: Linear models
Demo: Gradient descent
Demo: QR decomposition
Generalization and overfitting
Feature engineering and overfitting
Demo: Preprocessing crime data set
Demo: Predicting crime
Train, test, validate
Generalization
Underdetermined least squares and quadratic regularization
Demo: Singular value decomposition
The bootstrap and the bias variance tradeoff
Midterm exam
Loss functions for messy labels
Logistic regression and Support Vector Machines (SVMs)
Multiclass and ordinal regression
Optimization: subgradient method
Regularization for messy features
Regularization and interpretations
Demo: Proximal gradient starter code
Demo: Regularized Regression
Optimization: proximal gradient method
Demo: Proximal gradient method
Unsupervised learning
Missing data and PCA
Sparse PCA, NNMF, k-means
Optimization: AM and PALM
Missing value imputation using Gaussian copula
Demo: Impute GSS data using Gaussian copula
Introduction to AutoML
Demo: APIs of some AutoML systems
InterpretML: Explainable Boosting Machines (EBMs)
Telling stories with data
Nonlinear models
Decision trees
Learning from prototypes
Nearest neighbors
Smoothing
Graphs and networks
Trustworthy machine learning
Limitations of predictive modeling
Fairness
Interpretability
Causality
Review/overview
Midterm review
Final review
Julia
Demo tutorial: Julia syntax
GitHub
Demo tutorial: Github basics