Schedule

Readings in normal font should be completed and annotated ahead of lecture.
Readings in italic provide optional additional depth on the material.
Assignments are listed on the day when we suggest you begin working on them.
Assessments are listed on the day when they will occur.

Reading sources:

CN: Class notes written for this course, hosted here.
PDSH: The Python Data Science Handbook by @vanderplasPythonDataScience2016.
PDA: Python for Data Analysis, 3rd edition by @mckinneyPythonDataAnalysis2022.
BHN: Fairness and Machine Learning: Limitations and Opportunities by @barocasFairnessMachineLearning2023.

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	Assignments HW 1 (due 2/18)

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			Notes Class notes
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			Notes Class notes