Schedule

Week 1

Mon Feb. 09	Welcome!
Mon Feb. 09	Course introduction. What is machine learning? How will this class work? Introduction to data as a combination of signal and noise.
	Objectives Welcome! Theory			Notes Welcome slides
Wed Feb. 11	Data = Signal + Noise
Wed Feb. 11	Gaussian distribution, linear data with Gaussian noise. Likelihoods and log-likelihoods. Introduction to maximum likelihood estimation (MLE).
	Objectives Theory Experimentation	Reading Calculus review	Warmup Log and critical points	Notes Class notes

Week 2

Mon Feb. 16	Maximum likelihood, gradients
Mon Feb. 16	Calculating gradients and checking with Torch. Gradient descent for maximum-likelihood estimation in one dimension.
	Objectives Theory Experimentation		Warmup Partial derivatives, gradient	Notes Class notes	Assignments HW 1 (due 2/23)
Wed Feb. 18	Higher Dimensions
Wed Feb. 18	Linear-Gaussian model with many features. Matrix-vector notation for linear models. Loss function and gradient in higher dimensions.
	Objectives Theory Experimentation		Warmup No warmup problem -- 15 minute quiz at the beginning of class.	Notes Class notes

Week 3

Mon Feb. 23	Feature Maps and Regularization
Mon Feb. 23	Feature maps, overfitting, and regularization. Ridge regression and Lasso regression.
	Objectives Theory Experimentation		Warmup Overfitting in linear regression	Notes Class notes	Assignments HW 2 (due 3/2)
Wed Feb. 25	More on Overfitting and Model Complexity
Wed Feb. 25	Bias-variance tradeoff, double-descent.
	Objectives Theory Experimentation		Warmup Practice with expectation and variance	Notes Class notes

Week 4

Mon Mar. 02	Introducing Classification
Mon Mar. 02	Predicting binary labels with logistic regression
	Objectives Theory Experimentation		Warmup Gradient of signal function in logistic regression	Notes Class notes	Assignments Miniproject 1: Count regression (due 3/9)
Wed Mar. 04	Classification with Multiple Classes
Wed Mar. 04	Extending logistic regression to multiple classes.
	Objectives Theory Experimentation		Warmup No warmup problem -- 15 minute quiz at the beginning of class.	Notes Class notes

Week 5

Mon Mar. 09	Nearest Neighbor Methods and Kernel Classifiers
Mon Mar. 09	Another approach to classification with nonlinear decision boundaries.
	Objectives Theory		Warmup TBD	Notes TBD	Assignments HW 3 (due 3/16)

Week 6

Mon Mar. 16	Assessment of Classification Models
Mon Mar. 16	How to tell if your classifier is working.
	Objectives Experimentation		Warmup Gut checks for classification models	Notes Class notes	Assignments HW 4 (due 3/30)
Wed Mar. 18	Decision Theory in Classification
Wed Mar. 18	Using classifiers to make decisions, or not.
	Objectives Experimentation			Notes Class notes

Break

Mon Mar. 23	Spring break! No class.
Mon Mar. 23

Wed Mar. 25	Spring break! No class.
Wed Mar. 25

Week 7

Mon Mar. 30	Visit by David Rolnick (McGill)
Mon Mar. 30
	Objectives Social Impacts		Warmup TBD	Notes NA	Assignments Miniproject TBD (due 4/6)