class: center, middle, inverse, title-slide # Welcome to Statistical Learning ### Dr. D’Agostino McGowan --- layout: true <div class="my-footer"> <span> Dr. Lucy D'Agostino McGowan </span> </div> --- ## 👋 ## Lucy D'Agostino McGowan <i class="fa fa-envelope"></i> &nbsp; [mcgowald@wfu.edu](mailto:mcgowald@wfu.edu) <br> <i class="fa fa-calendar"></i> &nbsp; Tues/Thurs 9:30a-10:45a, 12:30p-1:45p --- class: middle, center Everything you will need will be posted at: # [bit.ly/sta-363-f20](http://bit.ly/sta-363-f20) --- # Statistical Learning Problems .pull-left[ * Identify risk factors for breast cancer ] .pull-right[  ] .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- # Statistical Learning Problems .pull-left[ <font color="grey"> * Identify risk factors for breast cancer </font> * Customize an email spam detection system ] .pull-right[ * **Data**: 4601 labeled emails sent to _George_ who works at _HP Labs_ * **Input features**: frequencies of words and punctuation ] | george | you | hp | free | ! | edu | remove --|-----|-----|----|-----|-----|----|------ **spam** | 0.00 | 2.26 | 0.02 | 0.52 | 0.51 | 0.01 | 0.28 **email** | 2.27 | 1.27 | 0.90 | 0.07 | 0.11 | 0.29 | 0.01 .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- # Statistical Learning Problems .pull-left[ <font color="grey"> * Identify risk factors for breast cancer * Customize an email spam detection system </font> * Identify numbers in handwritten zip code ] .pull-right[  ] .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- # Statistical Learning Problems .pull-left[ <font color="grey"> * Identify risk factors for breast cancer * Customize an email spam detection system * Identify numbers in handwritten zip code </font> * Establish the relationship between variables in population survey data ] .pull-right[ Income survey data for males from the central Atlantic region of US, 2009 <img src = "img/01/wage1.png" height = 350></img> ] .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- # Statistical Learning Problems .pull-left[ <font color="grey"> * Identify risk factors for breast cancer * Customize an email spam detection system * Identify numbers in handwritten zip code * Establish the relationship between variables in population survey data </font> * Classify pixels of an image ] .pull-right[ <img src = "img/01/land-use.png" height = 300></img> .small[ Usage `\(\in\)` {red soil, cotton, vegetation stubble, mixture, gray soil, damp gray soil} ] ] .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- ## ✌️ types of statistical learning .large[ * Supervised Learning * Unsupervised Learning ] --- ## Supervised Learning .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] * **outcome variable**: `\(Y\)`, (dependent variable, response, target) -- * **predictors**: vector of `\(p\)` predictors, `\(X\)`, (inputs, regressors, covariates, features, independent variables) -- * In the **regression problem**, `\(Y\)` is quantitative (e.g price, blood pressure) -- * In the **classification** problem, `\(Y\)` takes values in a finite, unordered set (survived/died, digit 0-9, cancer class of tissue sample) -- * We have **training data** `\((x_1, y_1), \dots, (x_N, y_N)\)`. These are observations (examples, instances) of these measurements --- ## Supervised Learning .question[ What do you think are some objectives here? ] -- ### Objectives * Accurately predict unseen test cases * Understand which inputs affect the outcome, and how * Assess the quality of our predictions and inferences .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] --- ## Unsupervised Learning .my-footer[.small[Dr. Lucy D'Agostino McGowan _adapted from slides by Hastie & Tibshirani_ ]] * No outcome variable, just a set of predictors (features) measured on a set of samples -- * objective is more fuzzy -- find groups of samples that behave similarly, find features that behave similarly, find linear combinations of features with the most variation -- * difficult to know how well your are doing -- * different from supervised learning, but can be useful as a pre-processing step for supervised learning --- class: center, middle # Let's go! --- ## Log in to RStudio Pro - **Note:** if you are off campus, you will need to use a [VPN to connect](https://is.wfu.edu/services/vpn/) - Go to [rstudio.hpc.ar53.wfu.edu:8787](rstudio.hpc.ar53.wfu.edu:8787) - Your username is your WFU username (typically the first part of your email) - The password is: **R2D2Star!** --- class: inverse <div class="countdown" id="timer_5f4687b4" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## <i class="fas fa-laptop"></i> `Penguin fun!` - Once you log on to RStudio Pro, find the project `01-welcome-penguins`, click it - In the Files pane in the bottom right corner, spot the file called `welcome-penguins.Rmd`. Open it, and then click on the "Knit" button. - Go back to the file and change your name on top (in the `yaml` -- we'll talk about what this means later) and knit again. - Then, scroll to the `plot` chunk, below **Palmer Penguins**. Instead of looking at the relationship between flipper length and bill length, plot the relationship between flipper length and bill **depth**. _Hint, look at the full dataset at the bottom of the document for variable names, update the captions to match your new plot_. Knit again & voila! --- ## Let's take a tour - class website .center[  ] - Concepts introduced: - How to find slides - How to find assignments - How to find RStudio Cloud - How to get help - How to find policies