(Class 16) Bernoulli, Binomial, and MLE
We will recap logistic regression, MLE, and discuss metrics for evaluating classification models
(Class 15) Bernoulli, Binomial, and MLE
We will discuss Bernoulli and Binomial distributed random variables, and using maximum likelihood to compute optimal coefficients for …
(Class 14) Interpreting coefficients and measures of accuracy
We will discuss how to interpret coefficients for a logistic regression model and measures of accuracy
(Class 13) Classification
We will discuss the disadvantages of linear regression and begin logistic regression
(Class 12) KNN regression
We will discuss the K nearest neighbor approach to regression.
(Class 11) Transform lab
We will discuss the impact of transforming the dependent variable in regression.
(Class 10) Transformations
We will discuss the impact of transforming the dependent variable in regression.
(Class 09) Bias-variance tradeoff
We will discuss the bias-variance tradeoff.
(Class 08) Cross-validation Lab
We will discuss the importance of having separate training and testing datasets and the bias-variance tradeoff.
(Class 07) Orthogonal projections as a method of optimization.
We will explore how orthogonality relates to optimization
(Class 06) Testing, training, and validation data, and the Bias-variance tradeoff.
We will discuss the importance of having separate training and testing datasets and the bias-variance tradeoff.
(Class 05) SSE optimization.
We will discuss optimzing regression parameters using SSE and the Hat Matrix
(Class 04) Lab on fitting simple, multiple, and polynomial models to Boston housing data.
Lab on fitting simple, multiple, and polynomial models to Boston housing data
(Class 03) Polynomial regression, optimizing parameters via SSE
We will explore polynomial regression for modeling non-linear relationships and optimizing parameters given data
(Class 02) Polynomial regression, testing and training
We will explore model and probabilistic forms of simple linear regression.
(Class 01) Technical setup and Matrix notation related to regression
We will take time to setup technical requirements for class (R and Github), and introduce matrix notation for simple linear regression
