Course Materials

• Syllabus
• Suggested Textbooks
  • Design and Analysis of Experiments with R by Lawson
  • R for Data Science by Wickham and Grolemund
  • Linear Algebra: Foundations of Machine Learning (BIOE 210 course website)
  • Surrogates by Gramacy
  • Reinforcement Learning: An Introduction by Sutton and Barto
• R Resources
  • RStudio: Open Source Desktop Edition
  • R Cheat Sheet
• Assignments
  • Homework Submission Guide
  • Homework 1 due Monday, 2/28 by 5pm.
    • GeneEditing.csv
  • Homework 2 due Monday, 2/28 by 5pm.
    • Slide Formatting Guide [Rmd]
  • Homework 3 due Friday, 4/29 by 5pm.
  • Homework 4 due Wednesday, 5/4 by 5pm.
    • The AlphaGo documentary is available here.
  • Homework 5 due Friday, 5/13 by 5pm.

Schedule

• W 1/19: Introduction [slides]
• F 1/21: The R Programming Language [slides] [Rmd]
• M 1/24: Factorial Designs 1: Main Effects [notes]
• W 1/26: Factorial Designs 2: Interactions [continued]
• F 1/28: Factorial Effects with Linear Models [slides][Rmd]
• M 1/31: Identifying Active Effects [slides] [Rmd]
• W 2/2: no class
• F 2/4: Fractional Factorial Designs 1 [slides] [Rmd]
• M 2/7: no class
• W 2/9: Fractional Factorial Designs 2 [slides] [Rmd]
• F 2/11: Fractional Factorial Designs 3 [slides] [Rmd]
• M 2/14: Replication [slides] [Rmd]
• W 2/16: Designs to Study Dispersion [slides] [Rmd]
• F 2/18: Nominal-the-Best Optimization [slides] [Rmd]
• M 2/21: Screening Designs I [slides] [Rmd]
• W 2/23: Screening Designs II [slides] [Rmd]
• F 2/25: Screening Designs III
• M 2/28: Exam Review [Exam 1 topics]
• W 3/2: Exam 1
  • Exam Corrections due 3/11 before 5pm.
• F 3/4: Steepest Ascent [slides]
• M 3/7: RSM I: Curvature [slides]
• W 3/9: RSM II: CCDs [slides]
• F 3/11: RSM III: Optimization [slides]
• M 3/14 - F 3/18: Spring Break
• M 3/21: RSM IV: Alternative Designs [slides]
• W 3/23: RSM V: The rsm Package (online lecture) [Rmd]
• F 3/25: RSM VI: Applications
• M 3/28: Class presentations
• W 3/30: Surrogates 1: Space Filling Designs [slides] [Rmd]
• F 4/1: Surrogates 2: Gaussian Process Regression [slides] [Rmd]
• M 4/4: Surrogates 3: Sequential Optimization [slides] [Rmd]
• W 4/6: Surrogates 4: Hyperparameter Tuning [slides] [Rmd]
• F 4/8: No class for EOH.
• M 4/11: Surrogates 5: Expected Improvement [slides] [Rmd]
• W 4/13: Homework 3 Workshop
• F 4/15: RL and MDPs [slides]
• M 4/18: Value Functions [slides]
• W 4/20: Rollout [slides]
• F 4/22: Applications: Deep Phenotyping
• M 4/25: Exam Review [Exam 2 topics]
• W 4/27: Exam 2
• F 4/29: Discounting and Q-factors [slides]
• M 5/2: Q-learning [slides]
  • AlphaGo Figures
• W 5/4: Summary [slides]