# DATA 1010: Introduction to Probability, Statistics, and Machine Learning

## Resources

## Class

September 5 (linear algebra and programming) [solutions video]

September 7 (more linear algebra and programming) [solutions video]

September 10 (linear algebra and SVD) [solutions video]

September 12 (determinants and matrix differentiation) [solutions video] [jupyter]

September 14 (machine arithmetic) [solution]

September 17 (numerical error) [solution]

September 19 (pseudorandom number generators, automatic differentiation) [solution]

September 21 (gradient descent, review) [solution]

September 24 (probability spaces) [solution]

September 26 (counting and random variables) [solution]

September 28 (conditional probability and independence) [solution]

October 1 (conditional probability) [solution]

October 3 (review) [solution]

October 5 (expectation) [solution]

October 10 (linearity of expectation) [solution]

October 12 (continuous distributions) [solution]

October 15 (conditional expectation) [solution]

October 17 (more continuous distributions, Bernoulli and binomial distributions) [solution]

October 19 (geometric, Poisson, exponential distributions) [solution]

October 22 (multivariate normal distribution) [solution]

October 24 (law of large numbers and CLT) [solution]

October 26 (CLT and multivariate CLT) [solution]

October 29 (Kernel density estimation) [solution]

October 31 (Kernel density estimation, review) [solution]

November 2 (Nonparametric regression) [solution]

November 5 (intro to classification, QDA) [solution]

November 7 (classification, LDA, Naive Bayes) [solution]

November 9 (logistic regression) [solution]

November 12 (support vector classification) [solution]

November 14 (kernelization for SVM, neural nets)

[November 17 – December 10] (neural nets, dimension reduction, likelihood ratio classification, intro to R,

`ggplot2`

,`dplyr`

, point estimation, confidence intervals, empirical CDF convergence, maximum likelihood estimation, hypothesis testing)December 12 (geographic maps in

`ggplot2`

, and logistic regression using`caret`

)

## Homework

PSet 1 - September 14 (linear algebra, SVD)

PSet 2 - September 21 (matrix differentiation, machine arithmetic, PRNGs)

PSet 3 - September 28 (automatic differentiation, gradient descent, probability, review problems)

PSet 4 - October 5 (review problems, probability)

PSet 5 - October 12 (expectation)

PSet 6 - October 19 (continuous distributions, conditional expectation)

PSet 7 - October 26 [files] (common distributions, central limit theorem)

PSet 8 - November 02 (probability review)

PSet 9 - November 09 (kernel density estimation, nonparametric regression, classification)

PSet 10 - November 16 (logistic regression, support vector machines, neural nets)

PSet 11 - November 30 (dimension reduction, likelihood ratio classification, data visualization and manipulation)

PSet 12 - December 17 (point estimation, confidence intervals, bootstrap, and maximum likelihood estimation)