Joanna Bieri

Applied Math Professor

Department of Economics

University of Redlands

DATA 301

Introduction to Machine Learning
Fall 2026

Class Meets:
Tuesday and Thursday, 2:40-3:55pm
Hall of Letters 101

Office Location:
Duke Hall #209
Phone: 909-748-8630
E-Mail: joanna_bieri@redlands.edu
(Email or Teams are my preferred contact methods)

Office Hours:
Click here for my schedule.
You can also email me for an appointment!

• Important Course Documents
  
  Course Syllabus
  
  Schedule of Topics
  NOTE: as the semester progresses we may change up the schedule a bit to suit our class pace and interests. The most recent schedule will be posted here.
  
  
• Software and Links
  
  Course GitHub Organization
  Textbook code notebooks
  Download Anaconda
  
  
• Daily Materials - Notes - Videos - Homework
  
  Each day I will post the lecture videos, notes, homework, reading, and other information. Make sure to check here for each day of class.
  • Day 1 - Tuesday - 9/1 - Click Here
    
    PRE-CLASS:

    Nothing to do before our first class! Just bring your laptop. Starting with Day 2, there will be videos, reading, and homework to complete before class. It is okay if you don't get all of it done, but you should attempt every part and keep trying until you are completely stuck and have questions to bring to class.

    CLASS TIME:

    Notes - Course Setup, Git, and the ML Landscape
    Slides - Course Setup, Git, and the ML Landscape
    Video - Setting up your computer and your GitHub repository
    Send me your GitHub username - I need this to set up your repository. Type just the username, not the whole web address.
    
    Once your repository is set up (see today's notes), HW_day1.ipynb will already be there. We will start it in class.
    
    Weekly Homework 1 - Fuel Economy - due Sunday 9/6 at 11:59pm. This covers Day 1 and Day 2, so start it after Thursday.
    
    Reading: Hands-On Machine Learning with Scikit-Learn and PyTorch
    Chapter 1, The Machine Learning Landscape. It is short and it is worth reading properly. Note that most of chapter 1 is in the book itself rather than the code notebook.
    

    ANNOUNCEMENTS:

    Finish your computer setup and your repository setup this week. Reach out if you get stuck, nobody loses points for a setup problem in week one!
    Send me your GitHub username as soon as you have one so I can create your repository.
    Start prepping for Day 2 - click on the Day 2 link and complete the PRE-CLASS materials.

    
    
  • Day 2 - Thursday - 9/3 - Click Here
    
    PRE-CLASS:

    Read Chapter 4 of Hands-On Machine Learning, the sections on Polynomial Regression and Learning Curves.
    Video - Regression as a Lab Bench
    

    CLASS TIME:

    Notes - Regression as a Lab Bench
    Slides - Regression as a Lab Bench
    
    Today we use regression as a work bench to study underfitting and overfitting, measure error with RMSE, and learn to read a learning curve.
    
    The notes have blue Q boxes for you to answer by hand, and a green You Try box of optional code. Bring your handwritten notes to class.

    ANNOUNCEMENTS:

    Weekly Homework 1 - Fuel Economy is due Sunday 9/6 at 11:59pm. It covers Day 1 and Day 2.
    HW_day2.ipynb is in your repository, do it as practice before the weekly homework.
    Start prepping for Day 3 - click on the Day 3 link.

    
    
  • Day 3 - Tuesday - 9/8 - Click Here
    
    PRE-CLASS:

    Read Chapter 4 of Hands-On Machine Learning, the section on Regularized Linear Models.
    Video - Bias, Variance, and Regularization
    

    CLASS TIME:

    Notes - Bias, Variance, and Regularization
    Slides - Bias, Variance, and Regularization
    
    Today we give Day 2's two failures their real names, bias and variance, and then learn the main tool for controlling them: Ridge, Lasso, and Elastic Net.
    
    The notes have nine blue Q boxes and a green You Try box of optional code. Short answers are in drop down boxes at the very bottom, but do the writing first. Bring your handwritten notes to class.

    ANNOUNCEMENTS:

    HW_day3.ipynb is in your repository, do it as practice.
    Weekly Homework 2 is due Sunday 9/13 at 11:59pm. It covers Day 3 and Day 4, so start it after Thursday.
    Start prepping for Day 4 - click on the Day 4 link.

    
    
  • Day 4 - Thursday - 9/10 - Click Here
    
    PRE-CLASS:

    Read Chapter 2 of Hands-On Machine Learning, the section on Create a Test Set, and Chapter 3, the section on Measuring Accuracy Using Cross-Validation.
    Video - Cross Validation for Real
    

    CLASS TIME:

    Notes - Cross Validation for Real
    Slides - Cross Validation for Real
    
    On Day 3 we picked alpha from one validation set and I told you it was fragile. Today we find out how fragile, and then fix it. K-fold cross validation, leakage, stratification, and nested cross validation.
    
    The notes have nine blue Q boxes and a green You Try box of optional code. Short answers are in drop down boxes at the very bottom, but do the writing first. Bring your handwritten notes to class.

    ANNOUNCEMENTS:

    Weekly Homework 2 - Wine Quality is due Sunday 9/13 at 11:59pm. It covers Day 3 and Day 4.
    HW_day4.ipynb is in your repository, do it as practice before the weekly homework.
    Start prepping for Day 5 - click on the Day 5 link.

    
    


We are learning machines!