BSAN 750 Data Mining and Machine Learning with R (Fall 2023)

Course info.

• Syllabus
  

• Room: CAPF 3056
  

• Time: T, H 11-12:15pm
  

• Office hour: by appointment

• Contact: shaobo.li@ku.edu

Links

• Programming tutorial: DataCamp

• Data repositories

  • UCI Machine Learning Repository

  • Kaggle

  • KDD Nuggets

• Top 10 Data Mining Algorithms

• After this class, I hope you have answers for most of these interview questions.

Recommended textbooks and tutorials

• Introduction to Statistical Learning [Online course videos]

• Elements of Statistical Learning (more theoretical)

• R for Data Science (extremely useful R programming skills)

• Another great online tutorial from UC Business Analytics by Bradley Boehmke

Lecture

These slides are built based on the recommended textbooks.

• Lecture 1

• Lecture 2

• Lecture 3

• Lecture 4

• Lecture 5

• Lecture 6

• Lecture 7

R Lab Notes

• Lab 1. Introduction to R

• Lab 2. Exploratory data analysis

• Lab 3. k-means and k-nearest neighbor

• Lab 4. Linear Regression

• Lab 5. Variable Selection and Regularized Method [Part1] [Part2]

• Lab 6. Logistic Regression

• Lab 7. CART

• Lab 8. Bagging, Random Forest, Boosting and XGBoost [Part1] [Part2]

• Lab 9. Neural Networks

HW and Exercise

• See Canvas