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!
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Important Course Documents
Course Syllabus
Final Project Information
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
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Daily Materials - Notes - Videos - Homework
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Day 1 - Tuesday - 9/1 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 1 - The Machine Learning Landscape
CLASS TIME:
Notes - Course Setup, Git, and the ML Landscape
Video - Setting up your computer and your GitHub repository
Slides - Course Setup, Git, and the ML Landscape
ANNOUNCEMENTS:
Send me your GitHub username - just the username, not the whole web address.
Set up your computer and your repository.
Start HW_day1.
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!
Weekly Homework 1 - Fuel Economy is due Sunday 9/6 at 11:59pm. It covers Day 1 and Day 2.
Start prepping for Day 2 - click on the Day 2 link.
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Day 2 - Thursday - 9/3 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 4 - Polynomial Regression and Learning Curves
CLASS TIME:
Notes - Regression as a Lab Bench
Video - Regression as a Lab Bench
Slides - Regression as a Lab Bench
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day2 - learning curves, under and overfitting.
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 (not graded) before the weekly homework.
Start prepping for Day 3 - click on the Day 3 link.
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Day 3 - Tuesday - 9/8 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 4 - Regularized Linear Models
CLASS TIME:
Notes - Bias, Variance, and Regularization
Video - Bias, Variance, and Regularization
Slides - Bias, Variance, and Regularization
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day3 - Ridge, Lasso, and Elastic Net.
HW_day3.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
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.
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Day 4 - Thursday - 9/10 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 2 - Create a Test Set, and ch. 3 - Measuring Accuracy Using Cross-Validation
CLASS TIME:
Notes - Cross Validation for Real
Video - Cross Validation for Real
Slides - Cross Validation for Real
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day4 - k-fold, leakage, and stratification.
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 (not graded) before the weekly homework.
Start prepping for Day 5 - click on the Day 5 link.
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Day 5 - Tuesday - 9/15 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 3 - Performance Measures
CLASS TIME:
Notes - Metrics Beyond Accuracy
Video - Metrics Beyond Accuracy
Reference Sheet - Classification Metrics
New this week: there is a box near the top of the notes on how to ask AI about code so that it teaches you instead of doing it for you.Slides - Metrics Beyond Accuracy
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day5 - confusion matrices, thresholds, ROC and PR curves.
HW_day5.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Weekly Homework 3 comes out after Thursday's class and covers Day 5 and Day 6. It is due Sunday 9/20 at 11:59pm.
Start prepping for Day 6 - click on the Day 6 link.
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Day 6 - Thursday - 9/17 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 5 - Training and Visualizing a Decision Tree, Making Predictions, Regularization Hyperparameters, and Decision Trees Have High Variance. Then ch. 6 - Voting Classifiers, Bagging and Pasting, and Random Forests
CLASS TIME:
Notes - Bagging and Random Forests
Video - Bagging and Random Forests
Slides - Bagging and Random Forests
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day6 - decision trees, bagging, and random forests.
Weekly Homework 3 - Bank Marketing is due Sunday 9/20 at 11:59pm. It covers Day 5 and Day 6.
HW_day6.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Tuesday 9/22 is a review day in class, a chance to catch up on Days 1 to 6. Bring questions!
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Review Day - Tuesday - 9/22 - Click Here
PRE-CLASS:No video and no new reading this week.
CLASS TIME:
Go back through Days 1 to 6, the Q boxes you left blank, and any part of the weekly homework you want to talk about.
Bring at least one question!Catch up and reflect, no new topic. We work on your questions.
ANNOUNCEMENTS:
Get any of the daily HW files you are behind on running.Nothing is due this week. Weekly Homework 4 comes out after Day 8 and is due Sunday 10/11 at 11:59pm (the deadline was extended).
Start prepping for Day 7 - click on the Day 7 link.
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Day 7 - Thursday - 9/24 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 6 - Boosting (both AdaBoost and Gradient Boosting) and Stacking
CLASS TIME:
Notes - Boosting: Gradient Boosting and XGBoost
Video - Boosting
Slides - Boosting
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day7 - gradient boosting, early stopping, and XGBoost.
HW_day7.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Weekly Homework 4 comes out after Day 8 on Tuesday and covers Day 7 and Day 8. It is due Sunday 10/11 at 11:59pm (the deadline was extended).
Start prepping for Day 8 - click on the Day 8 link.
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Day 8 - Tuesday - 9/29 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 2, all of it
CLASS TIME:
Notes - The Full Pipeline: Preprocessing, Search, and Model Selection
Video - The Full Pipeline
Slides - The Full Pipeline
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day8 - building one pipeline, and letting a search pick the settings.
Weekly Homework 4 - Bank Pipeline is due Sunday 10/11 at 11:59pm (the deadline was extended). It covers Day 7 and Day 8.
HW_day8.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Thursday 10/1 is a catch-up day in class, to review for Exam 1. Exam 1 goes out after class that day and is due Monday 10/5 at 11:59pm.
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Review Day - Thursday - 10/1 - Click Here
CLASS TIME:Catch up and review for Exam 1, no new topic.
ANNOUNCEMENTS:
Exam 1 went out after class and is due Monday 10/5 at 11:59pm.
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Rest and Recover Day - Tuesday - 10/6 - Click Here
ANNOUNCEMENTS:No class today. This is a rest and recover day.
Day 9 is Thursday 10/8.
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Day 9 - Thursday - 10/8 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 9, From Biological to Artificial Neurons, and ch. 10, PyTorch Fundamentals
CLASS TIME:
Notes - From Logistic Regression to a Neuron: Tensors and Autograd
Video - From Logistic Regression to a Neuron
Slides - From Logistic Regression to a Neuron
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day9 - gradient descent by hand, and training a neuron on the penguins.
Class on Tuesday 10/6 is cancelled. Day 9 is now Thursday 10/8.
Hand in your handwritten Exam 1 essays in class on Thursday 10/8.
HW_day9.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Weekly Homework 5 - Life Expectancy covers Day 9 and Day 10. It is now due Sunday 10/18 at 11:59pm.
Tuesday 10/13 is Fall Break, no class. Day 10 is Thursday 10/15.
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Fall Break - Tuesday - 10/13 - Click Here
ANNOUNCEMENTS:Fall Break, no class.
Day 10 is Thursday 10/15.
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Day 10 - Thursday - 10/15 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 10, Implementing a Regression MLP, and Implementing Mini-Batch Gradient Descent using DataLoaders
CLASS TIME:
Notes - MLPs in PyTorch: Writing the Training Loop by Hand
Video - MLPs in PyTorch
Slides - MLPs in PyTorch
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day10 - a deeper MLP, and the mini-batch training loop.
HW_day10.ipynb is in your repository, do it as practice (not graded) before the weekly homework.
Weekly Homework 5 - Life Expectancy is now due Sunday 10/18 at 11:59pm. It covers Day 9 and Day 10.
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Day 11 - Tuesday - 10/20 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: Appendix A (Autodiff), and ch. 11, Glorot and He Initialization, and Better Activation Functions
CLASS TIME:
Notes - Backpropagation, Activations, and Initialization
Video - Backpropagation, Activations, and Initialization
Video - Backpropagation and Review of Derivatives by Hand
Take the Day 11 Pre-Class Quiz on Canvas.
Slides - Backpropagation, Activations, and Initialization
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day11 - backpropagation by hand, and a deep network with Kaiming initialization.
HW_day11 is practice, not graded.
NEW: solutions for Days 2 to 11 are in your repository. Try the problems first, then check your work!
Weekly Homework 6 - Diamond Prices (Days 11 and 12) is due Sunday 11/1 at 11:59pm.
More about final project teams and the proposal soon.
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Catch-Up Day - Thursday - 10/22 - Pause and Get Caught Up
No new material, we are pausing to get caught up! Details coming soon.
ANNOUNCEMENTS:Day 12 is now Tuesday 10/27.
Weekly Homework 6 is due Sunday 11/1 at 11:59pm.
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Day 12 - Tuesday - 10/27 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 11, Batch Normalization, Faster Optimizers, and Learning Rate Scheduling
CLASS TIME:
Notes - Optimizers, Learning Rate Schedules, and Batch Normalization
Video - Optimizers, Learning Rate Schedules, and Batch Normalization
Take the Day 12 Pre-Class Quiz on Canvas.
Slides - Optimizers, Learning Rate Schedules, and Batch Normalization
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day12 - Adam and a learning rate schedule on a deep network.
HW_day12 is practice, not graded.
Weekly Homework 6 - Diamond Prices (Days 11 and 12) is due Sunday 11/1 at 11:59pm.
More about final project teams and the proposal soon.
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Day 13 - Thursday - 10/29 - Click Here
PRE-CLASS:Reading: Hands-On Machine Learning: ch. 11, Avoiding Overfitting Through Regularization (Dropout, and l1 and l2 Regularization), and ch. 4, Early Stopping
CLASS TIME:
Notes - Regularization for Neural Networks: Early Stopping and Dropout
Video - posted soon
Take the Day 13 Pre-Class Quiz on Canvas.
Slides - Early Stopping and Dropout
ANNOUNCEMENTS:
Questions from the notes.
Work on HW_day13 - spot overfitting, add dropout, add early stopping.
HW_day13 is practice, not graded.
Weekly Homework 7 (Days 13 and 14) is due Sunday 11/8 at 11:59pm.
More about final project teams and the proposal soon.
Each day I will post the lecture videos, notes, homework, reading, and other information. Make sure to check here for each day of class.
The daily practice problems (HW_dayN.ipynb) are practice. They are not graded and you do not turn them in. The Weekly Homework is what gets graded, and it assumes you have done the practice problems.
Work the blue Q boxes in the notes by hand and bring them to class. Code cells are tagged RUN THIS, LEARN TO WRITE THIS, or DEMO ONLY, so you know what to copy and what to learn. -
Day 1 - Tuesday - 9/1 - Click Here
We are learning machines!
