schedule.md

title	Schedule
layout	page
bibliography	refs.bib

Before going through Unit 1, please review Unit 0. This material will not be covered in class and it is expected that if you are not already familiar with it, you will learn it by the end of the first week.

Note This schedule is subject to changes throughout the term. Please check this schedule on a weekly basis. Changes will only effect exams in rare circumstances. You should check the commit history on a weekly basis to check if any changes have been made.

Date	Unit Material	Topics	Notes
Week 1
Mon Sept 15	Unit 1	Intro, Unit 1.1 (Probability models)
Wed Sept 17	Unit 1	Unit 1.2 (Conditional probability, marginalization)
Fri Sept 19	Unit 1	Python, working with tabular data	Course survey
Week 2
Mon Sept 22	Unit 2	Unit 2.1 (Expectation, conditional expectation, variance)
Wed Sept 24	Unit 2	Unit 2.2 (Continuous distributions, Normal distribution and CLT)	I didn't cover continious distributions in detial. This will not be so important for future sections.
Fri Sept 26	Unit 3	Unit 2.3 (linear regression with binary predictor)	I introduced the idea of sample distribution in the context of linear regression with binary predictor
Week 3
Mon Sept 29	Unit 3	Unit 3.1 cont. (estimators, bias, consistency) Unit 3.2 (linear regression with normal predictor)
Wed Oct 1	Unit 3	Unit 3.2 cont. (Least squares, Correlation, coefficient of determination)
Fri Oct 3	Unit 3	Unit 3 leftover if needed + Exploratory data analysis/project discussion	End of midterm material
Week 4
Mon Oct 6	Review		Come with questions!
Wed Oct 8	MIDTERM IN CLASS
Fri Oct 10	Work on project
Week 5
Mon Oct 13	Unit 4	Unit 4.1 (Multiple predictor regression examples, interpreting regression coefficients)
Wed Oct 15	Unit 4	Unit 4.2 (Simpsons paradox, effects of adding predictors)
Fri Oct 17	Unit 4	Unit 4 (colinearity, the joint sample distribution)
Week 6
Mon Oct 20	Unit 4	Unit 4 (models with catagorical predictors)
Wed Oct 22	Unit 4	Unit 4 (analysis of variance)
Fri Oct 24		Work on project
Week 7
Mon Oct 27	Unit 5	Unit 5 (interactions, residual plots)
Wed Oct 29	Unit 5	Unit 5 (feature maps, polynomial regression, biasn variance tradeoff revisted)
Fri Oct 31	Unit 6	Unit 5 (fourier models) Unit 6.3 (regularization)
Week 8
Mon Nov 3	Unit 6	Unit 6 (bayesian inference for bernoulli model, beta distribution laplace rule of succession)
Wed Nov 5	Unit 6	Unit 6 (bayesian linear regression)
Fri Nov 7	Unit 6	Unit 6 (connection between bayesian inference and regularization)	Last day of final material
Week 9
Mon Nov 10	Unit 7	statistics vs. machine learning, the kernel trick
Wed Nov 12	Unit 7	Gaussian processes as a Bayesian forier model
Fri Nov 14	Unit 7	Logistic regression, project
Week 1
Mon Nov 17	Review