GBUS 738 Lecture Material
All lecture slides, video recordings, and extra materials will be posted here throughout the semester.
Lesson 1 - Introduction
In this lesson we will go over the objectives of GBUS 738, define
machine learning and discuss its broad application to our daily lives,
and cover the fundamentals of R programming.
Slides
Videos
Lecture - Machine Learning Algorithms
Lesson 2 - Data Analysis
In this lesson we will cover Cross-Industry Standard Process for Data Mining (CRISP-DM) and data analysis with the tidyverse
Slides
Videos
Lecture - Analytics Process CRISP-DM
Lecture - Data Terminology and Optimal Layout for Analysis
R Tutorial - Data Analysis with dplyr
Lesson 3 - Data Visualization
In this lesson we will cover joining data frames, pivoting and reshaping data, and data visualization.
Videos
R Tutorial - Joining Data Frames
R Tutorial - Reshaping Data
R Tutorial - Data Visualization
Lesson 4 - Feature Engineering
In this lesson we will cover Feature Engineering with the recipes package
Slides
Videos
Lecture - Data Analysis Project Instructions
Lecture - Machine Learning Process
R Tutorial - Data Resampling
R Tutorial - Feature Engineering
Lesson 5 - Linear Regression
In this lesson we will begin the predictive modeling section of the course starting with linear regression.
Slides
Videos
Lecture - Linear Regression
R Tutorial - Linear Regression with tidymodels
Lesson 6 - Logistic Regression
In this lesson we will introduce classification with logistic regression and how to assess the performance of a classification model.
Slides
Videos
Lecture - Linear Regression Review
Lecture - Logistic Regression
Lecture - Evaluating Classification Models
R Tutorial - Logistic Regression with tidymodels
Lesson 7 - Discriminant Analysis
In this lesson we will introduce linear and quadratic discriminant analysis and the k-nearest neighbor algorithm for classification and regression.
Slides
Videos
Lecture - Conditional Probability and Bayes Theorem
Lecture - Discriminant Analysis
Lecture - KNN and Hyperparameter Tuning
R Tutorial - Discriminant Analysis and KNN with
tidymodels
Lesson 8 - Tree-Based Models
In this lesson we will learn about decision trees, bootstrap
aggregation, ensemble modeling, and random forests.
Slides
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