Stat 652: Statistical Learning
Department of Statistics and Data Science, CSU East Bay
Fall 2026:
Week 7:
- On Monday this week we will complete the discussion of Rules based Decision Tree and Tuning. We will discuss Neural Networks and Clustering on Wednesday. If you are interested, in Chapter 7 of Lantz 4ed there is a discussion of SVMs and in Chapter 8 there is a discussion of Association Rules.
- Presentation: images.pdf
- ANN.html
- ANN.qmd
- MLwR4e Chapter_07.R
- MLwR_v2_07.r
- MLwR_v2_07_h2o.r
- concrete.csv
- R Project: Chap07.zip
- YouTube: Neural Networks Demistified Video
- Image, Translation, Speech:
- Microsoft: Cognitive Services Translator Speech API
- Google: Cloud Services CoLab AIStudio Google
- Amazon: AWS Deep Learning SageMaker
- h2o: Deep Learning Deep Learning with R Tutorials
- Nvidia: NVidia’s GPUs - The Engine of Deep Learning
- Baidu: Baidu Reseach
- Tensorflow
- PyTorch
- Presentation:
- SVM.html
- SVM.qmd
- MLwR4e Chapter_07.R
- MLwR_v2_07.r
- MLwR_v2_07_h2o.r
- letterdata.csv
- R Project: Chap07.zip
- Website Spotlight: UCI MLR mnist dataset
- kaggle Competition: Digit Recognizer
- Spotlight paper: EMNIST
- Website Spotlight: EMNIST Dataset
- Website Spotlight: cedar
- Website Spotlight: IMAGENET
- Website Spotlight: Fashion NIST
- Presentation:
- Presentation: Transactional Data and Association Rules
- Association.html
- Association.qmd
- MLwR4e Chapter_08.R
- MLwR_v2_08.r
- groceries.csv
- R Project: Chap08.zip
- Spotlight book: Hands on Machine Learning
- Spotlight book: Dive into Deep Learning
- Spotlight book: Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
- Spotlight book: Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy
- Spotlight book: Deep Learning: Foundations and Concepts
Week 6:
- Homework: Homework 4 has been posted.
- Handout: The Five Steps: steps01.docx
- Presentation: images.pdf
- Further Examples: This R Project contains new example that use the new Workflow package that is part of the tidymodels collection of packages. Cross-validation is used.
- Presentation: Boosting, Bagging, Random Forests
- Tuning.html
- Tuning.qmd
- R Project: Chap11.zip This code takes a long time to run. Try this after the Chap05 code.
- Presentation:
- Rules.html
- Rules.qmd
- mushrooms.csv
- R Project: Chap05.zip
- Software Spotlight: H2O.ai H2O Driverless AI H2O AutoML redwine example
- ML Explainability
- Software Spotlight: Dataiku
Week 5:
- Homework: Homework 3 has been posted.
- Midterm: The Midterm has been posted. See the Homework link. The titanic data is used.
- TidyModels book Start Try the 5 steps under Getting Started on the left.
- Step-by-step Data Science Machine learning in R with tidymodels
- See Rebecca Barter’s blog post Tidymodels: tidy machine learning in R.
- See Olivier Gimenez’s blog post Experimenting with machine learning in R with tidymodels and the Kaggle titanic dataset.
- Presentation: images.pdf
- Quarto Project: Chap06.zip updated Logistic Regression code, compare ROCs
- Presentation:
- Website Spotlight: Rseek
- Presentation: images.pdf
- Presentation: images.pdf
- What is a VCorpus? StackExchange
- tm Vignettes
- Hint: Recall from class that some people running R on Windows had a fonts problem. To solve the problem we added a line to the code giving the third DTM to the first. Since all of the steps used to create the first DTM are also done for the third DTM.
- compare the result
- sms_dtm
- sms_dtm2
- sms_dtm3
- sms_dtm <- sms_dtm3
- Homework Solutions: TidyModels Examples: All of the code below is work in progress. I need to add my comments still to my notebooks. The code below is made available as an extra set of code using the new TidyModels package. The code needs to be updated to use the workflows() and remove the bake() function.
- OpenAI Tools: Use your university email to log in to use ChatGPT and Codex.
Week 4:
- Homework: Homework 2b has been posted.
- Before starting to run any ML algorithms on the NHANES data you should investigate what is in the dataset. In particular which variables are numeric and which ones are categorical. You should also check to see how much data is missing from each variable.
- NHANES_ver01.qmd
- Quiz: Quiz 1 due next week on Friday September 18.
- Datasets:
- Presentation: images.pdf
- CART.html
- CART.qmd
- MLwR4e Chapter_06.R
- MLwR_v2_06.r
- whitewines.csv
- redwines.csv
- Quarto Project: Chap06.zip
- Spotlight blog: Beginner’s guide to machine learning in R (with step-by-step tutorial)
- YouTube: R package reviews | glmulti | Find The Best Model !
- Spotlight Software: Tidyverse
- Spotlight Software: Tidymodels rsample
- Spotlight Software: caret
- Spotlight Software: easytats report performance
- Website Spotlight: UC Business Analytics R Programming Guide Very Nice!!!
- Website Spotlight: UC-r Logistic Regression Tutorial
- Website Spotlight: UC-r Resampling Methods Tutorial
- Spotlight blog: How to perform Logistic Regression in R
- Website Spotlight: Generalized Linear Models
- YouTube: The tradeoff between Sensitivity and Specificity
- YouTube: ROC Curves Video See minute 7.
- YouTube: GBM
Week 3:
- Homework: Homework 2a has been posted.
- Quiz: Quiz 1 has been posted under Assignments.
- The following presentations are based on the chapters in the Lantz Machine Learning with R, Fourth Edition
- Presentation: images.pdf
- Regression.html
- Regression.qmd
- MLwR4e Chapter_06.R
- MLwR_v2_06.r
- challenger.csv
- insurance.csv
- Quarto Project: Chap06.zip
- Website Spotlight: UC-r Linear Regression Tutorial
- Website Spotlight: UC-r Linear Model Selection Tutorial
- Presentation: images.pdf
- Presentation:
- Presentation: An example of running knn in the Tidyverse using the Tidymodels package.
- Spotlight blog: Tidymodels: tidy machine learning in R
- Spotlight Youtube: Jelena Ilic: Modeling, Tidyverse Way
- Microsoft Copilot Prompt:
I am a Statistics MS Student in a Machine Learning course. We are discussing the use of Training and Testing Dataset with Machine Learning Algorithms. Please provide a step by step example of how to use the Training and Testing Dataset with the kNN algorithm in R using the Tidyverse and Tidymodels packages. Use the modern R pipe |>. Please provide the R code in a .qmd file with embed-resources: true.
- Microsoft Copilot Tools:
- Microsoft Copilot Use your university email to log in to use Copilot and Office365.
- Spotlight Books:
Week 2:
- Assignment: Homework 1 has been posted. Download the .zip file into your class directory and Homework sub-directory, rename the .qmd file with your lastname and firstname, and add your name in the yaml header.
- Book: mdsr3e
- Presentation:
- Quarto Notebook:
- Quarto Notebook:
- Ch09 Statistical Foundations.html using the infer R package
- Ch09 Statistical Foundations.qmd
- Spotlight Software:
- Spotlight Blog post: Prime Hints For Running A Data Project In R
- Spotlight blog: Data Science Central
- Spotlight blog: The 10 Statistical Techniques Data Scientists Need to Master
- Spotlight blog: MACHINE LEARNING TRENDS IN 2023
- Spotlight blog: 2023 emerging AI and Machine Learning trends
- posit::global 2026: posit::conf 2026
- posit::global 2025: posit::conf 2025
- posit::global 2024: posit::conf 2024
- posit::global 2023: posit::conf 2023
- RStudio::global 2022: rstudio::conf 2022
- RStudio::global 2021: rstudio::global 2021
- Spotlight R package: DataExplorer AutoEDA
- Gemini Prompt:
I am a Statistics MS Student in a Machine Learning course. We are using R and RStudio for the homework.
The instructor has asked us to start and complete each homework assignment in an R Project. Please make a step by step list for how to create an R Project in R Studio.
- Google AI Tools:
- Spotlight Books:
- Lantz Machine Learning with R, 4ed, datasets and code Download the code onto your computer.
- ModernDive (v2)
- Tidy Modeling with R
- Hands on Machine Learning with R
- Introduction to Statistical Learning
- tidymodels
Week 1:
- Book: mdsr3e
- Assignment: Homework 1 has been posted.
- Presentation:
- Spotlight Software:
Week 0:
- Learning R:
- Learning Python:
- Learn SQL:
- Excellent References:
- Data Science:
- Socviz
- r4ds
- ModernDive
- Yarrr!
- R Data Science Essentials
- Python Data Science Essentials
- Deep Learning Made Easy with R
- Doing Data Science
- Data Science from Scratch
- What is Data Science? (fast easy read)
- Ethics and Data Science (fast easy read)
- Data Driven (fast easy read)
- R Markdown: The Definitive Guide
- Reading related to the Digital Economy:
- More Big Picture: