Section outline

  • Collapse all Expand all

  • Thursday, April 2, 2020; 1:00 PM - 2:30 PM

    Lecture: What is Machine Learning? Introductory concepts

    Data types: continuous, binary, categorical, survival, count: introduce datasets for each to be used in the course. Supervised vs. Unsupervised learning. Overview of prediction techniques for supervised learning. Regression tree and support vector machine overview example in R for binary data – discuss overfitting, tuning parameters. Discuss explanatory models vs. black box prediction (comparing regression tree with SVM). Provide an initial look at how to choose a predictive model.

    Faculty:  John Kornak

    Please watch 2019 recorded lectures

  • Thursday, April 2, 2020; 2:45 PM - 3:45 PM

    Lab

    Faculty:  John Kornak

    • Assignment: Optional narrative description

    • Biostat216Lab1v2 File
    • Biostat216Lab1v2 File

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216Lab1v2 solutions File
    • Biostat216Lab1v2 solutions File
    • lab1 File

  • Thursday, April 9, 2020; 1:00 PM - 2:30 PM

    Lecture: Model/algorithm evaluation/choice

    Model/algorithm evaluation for each data type: e.g. for binary accuracy, AUC for ROC, and Gini Index. Concept of “predictor importance”. Training-test, training-validation-test, cross-validation, nested cross-validation, reserving data -- cross-validate ~2/3 of data and reserve ~1/3 for model evaluation.

    Faculty:  John Kornak

    Please watch 2019 recorded lectures

  • Thursday, April 9, 2020; 3:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak

    • Assignment: Optional narrative description

    • Biostat216Lab2v3 File
    • Biostat216Lab2v3 File
    • lab2 File

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216Lab2v3solutions File
    • Biostat216Lab2v3solutions File

  • Thursday, April 9, 2020; 3:45 PM - 4:45 PM

    Discussion Section:

    Faculty:  John Kornak


  • Thursday, April 16, 2020; 1:00 PM - 2:30 PM

    Lecture: Linear Algebra, Optimization and Validation


    Faculty:  John Kornak

    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR lecture3 v1 File
    • MachineLearningInR lecture3 v1 File
    • MachineLearningInR lecture3 v2revisedPostLecture File
    • MachineLearningInR lecture3 v2revisedPostLecture File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date:  Due Monday, April 27

    • Biostat216HW1v3 File
    • Biostat216HW1v3 File
    • Homework Assignment 1

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216HW1v3solutions File
    • Biostat216HW1v3solutions File

  • Thursday, April 16, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak

    • Assignment: Optional narrative description

    • Biostat216Lab3v1 File
    • Biostat216Lab3v1 File

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216Lab3v1solutions File
    • Biostat216Lab3v1solutions File
    • lab3 File

  • Thursday, April 23, 2020; 1:00 PM - 2:30 PM

    Lecture:  Support Vector Machines
    Support Vector Machines (linear and nonlinear) – background methodology, theory and application in R (e1071, kernlab) – illustrate training-validation-test

    Faculty:  John Kornak

    Please watch 2019 recorded lectures

  • Thursday, April 23, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak

    • Assignment: Optional narrative description

    • Biostat216Lab4v1 2020version File
    • Biostat216Lab4v1 2020version File
    • svmPlotFunction File
    • dementia File

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216Lab4v1 solutions 2020version File
    • Biostat216Lab4v1 solutions 2020version File
    • lab4 updated2020 File

  • Thursday, April 23, 2020; 3:45 PM - 4:45 PM

    Discussion Section:

    Faculty:  John Kornak

    • Required Reading:

    • nejmra1814259 File

  • Thursday, April 30, 2020; 1:00 PM - 2:30 PM

    Lecture: Regression Trees and Random Forests

    Motivation for, and underpinnings of, the classification and regression tree paradigm will be detailed along with illustrative applications.  Shortcomings of the technique will be identified and the means whereby these can be redressed by random forests described.  Properties of random forests will be highlighted and examples presented.

    Faculty:  Mark Segal


    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR lectureX Trees Forests File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date: Monday, May 11th.

    • Biostat216HW2v1 File
    • Biostat216HW2v1 File
    • Homework Assignment 2

    • Assignment Answer Key (Access restricted to registered students):

    • Biostat216HW2v1solutions File
    • Biostat216HW2v1solutions File

  • Thursday, April 30, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak
    • lab5treesNoOutput File
    • dementia2 File
    • dataPrep File

    • Assignment Due Date:  (e.g.,  1 pm on April 1 2013” or  “At the beginning of Small Group Section”)

    • Assignment Answer Key (Access restricted to registered students):

    • lab5trees File
    • lab5trees File
    • lab5trees File
    • lab5trees File

  • Thursday, May 7, 2020; 1:00 PM - 2:30 PM

    Lecture: Concepts of boosting and gradient boosting
    Concepts of boosting, gradient boosting

    Faculty:  Gilmer Valdes

    Please watch 2019 recorded lectures

    • Session Slides:

    • Boosting 2019 File
    • Boosting 2019 File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date:  Monday, May 18th.

    • Homework3 File
    • Homework3 File
    • dementia2 File
    • Homework Assignment 3

    • Assignment Answer Key (Access restricted to registered students):

    • Homework3solutions File
    • Homework3solutions File
    • Homework3solutions File

  • Thursday, May 7, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak 
    • lab6boosting File

    • Assignment Due Date:  (e.g.,  1 pm on April 1 2013” or  “At the beginning of Small Group Section”)

    • Assignment Answer Key (Access restricted to registered students):

    • lab6boosting File
    • lab6boosting File
    • lab6boosting File

  • Thursday, May 7, 2020; 3:45 PM - 4:45 PM

    Discussion Section:

    Faculty:  John Kornak

  • Wednesday, May 13, 2020; 10:00 AM - 12030 PM

    Lecture:  Penalized regression methods

    Motivation via multiple linear regression, ridge regression, lasso, elastic net, extensions/inference – discuss in context of continuous, binary, categorical, continuous, and survival endpoints -- illustrate training-validation-test

    Faculty:  Mark Segal

    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR lecture7 Lasso File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date:  Friday, May 29th.

    • Homework4 File
    • Homework4 File
    • Homework Assignment 4

    • Assignment Answer Key (Access restricted to registered students):

    • Homework4solutions File
    • Homework4solutions File
    • Homework4solutions File
    • Homework4solutions File

  • Thursday, May 14, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak

    Location: MH-1407  

    • CrossValidation File
    • CrossValidation File
    • lab7penalizedRegression File
    • lab7penalizedRegression File
    • lab7penalizedRegression File
    • ispy1doctored File

    • Assignment Due Date:  (e.g.,  1 pm on April 1 2013” or  “At the beginning of Small Group Section”)

    • Assignment Answer Key (Access restricted to registered students):

    • lab7penalizedRegressionSolutions File
      Available until end of June 30, 2020

  • Wednesday, May 20, 2020; 10:00 AM - 12:00 PM

    Lecture: Unsupervised learning: Clustering and Data reduction algorithms
    Clustering algorithms – K-means, Silhouette measure, Mixture models, hierarchical clustering – measures for choosing number of clusters. Data reduction algorithms – Principal components analysis (PCA) and Independent components analysis (ICA) -- measures for choosing number of components

    Faculty: John Kornak

    Please watch 2019 recorded lectures

    • Session Slides:

    • MachineLearningInR unsupervisedV1 File
    • MachineLearningInR unsupervisedV1 File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date:  June 3rd, 2020

    • Project Assignment

    • Assignment Answer Key (Access restricted to registered students):

  • Thursday, May 21, 2020; 2:45 PM - 3:45 PM

    Lab:

    Faculty:  John Kornak

    Location: MH-1407   

    • lab8unsupervised File
    • lab8unsupervised File
    • lab8unsupervised File
    • ispy1doctored2 File

    • Assignment Answer Key (Access restricted to registered students):

    • lab8unsupervisedSolutions File
      Available until end of June 30, 2020
    • lab8unsupervisedSolutions File
    • lab8unsupervisedSolutions File
      Available until end of June 30, 2020
    • lab8unsupervisedSolutions File
      Available until end of June 30, 2020

    • Assignment Due Date:  (e.g.,  1 pm on April 1 2013” or  “At the beginning of Small Group Section”)

  • Thursday, May 28, 2020; 1:00 PM - 2:30 PM

    Lecture:  Applications lecture: clustering and classification of high-dimensional data in a genomic context
    Clustering and classification of high-dimensional data in a genomic context

    Faculty:  Adam Olshen

    Please watch 2019 recorded lectures 

    • Session Slides:

    • genomics 2019 05 30 File
    • genomics 2019 05 30 File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

    • Assignment Due Date: Monday, June 8th.

    • Homework5 File
    • Homework5 File
    • Homework5 File
    • dementia2 File
    • Homework Assignment 5

    • Assignment Answer Key (Access restricted to registered students):

    • Homework5solutions File
    • Homework5solutions File
    • Homework5solutions File
    • Homework5solutions File

  • Thursday, May 28, 2020; 2:45 PM - 3:45 PM

    Lab

    Faculty:  John Kornak

    • Assignment Due Date:  (e.g.,  1 pm on April 1 2013” or  “At the beginning of Small Group Section”)

    • Assignment Answer Key (Access restricted to registered students):

  • Thursday, May 28, 2020; 3:45 PM - 4:45 PM

    Discussion

    Faculty:  John Kornak

    • Session Slides:

    • Required Reading:

  • Thursday, June 4, 2020; 1:00 PM - 2:30 PM

    Lecture: The rtemis Machine Learning R package

    Overview of the rtemis package: emphasizing ability to (and importance thereof) perform nested cross-validation

    Faculty:  Efstathios Gennatas

    Please watch 2019 recorded lectures

    • Session Slides:

    • Biostat216 rtemisML EDG File

    • Session Audio/Video Recording (Access restricted to registered students):

    • Watch 2019 Recording URL
      Not available unless: You belong to a group in Registered Students Only

  • Thursday, June 4, 2020; 2:45 PM - 3:45 PM

    Lab

    Faculty:  John Kornak