Where are we and what should I be doing?
April 14, 2025
We finished the simple mnist example.
We are about to read through the torch example from the ISLR lab.
Homework 4 is on the webpage.
Due May 2.
Note that if you are not sure what to do for a project,
then simply working through hw4 and doing some more would be great.
For example, you could used the used cars data but use more features than just x=(mileage,year).
Note also that there is a usedcars.csv that has more features and more observations.
April 4, 2025
About to go over the optimization section in the neural networks notes.
March 27, 2025
We finished the example where we did the California housing example with
a single tree, random forests, and boosting.
Next time we will finish up trees and then get started with neural nets.
We won't go into detail (now) on all the remaining tree notes, just note what
node purity is and what gradient boosting is, and then do the final example with
the python version of the xgboost package.
Homework 3 is due April 4.
March 20, 2025
We just finished the section trees, a summary in the trees notes.
March 6, 2025
We got to the last section of the notes on regularized logistic regression.
Next up is classification metrics and then on to the stars of the show:
Ensembles of trees and neural networks!!
February 27, 2025
We are just about to do the Elastic net which is the second last section of
Regularized Linear Regression.
Let's make homework 2 due March 7.
February 25, 2025
About to do AIC/BIC in the regression notes.
February 20, 2025
We finished the notes "MLE and a little optimization" and are about to start
"Linear Models and Regularization".
Homework 2 is on the webpage and due March 3.
February 14, 2025
Last class we went through the notes "More Probability, Continuous Random Variables"
and got to the section 3, the Bernoulli MLE in the notes "MLE and a little optimization".
Homework 1 is due February 21st which is this coming Friday.
You don't have to make the homework pretty, just show enough results/pictures
that we can tell you that you made an effort.
Let's agree that you can and should use AI (chatGPT, claude) for the homework and throughout the class
but not on the final project.
February 8, 2025
The week of January 28,30 was a right off because Rob could not talk.
For now we have skipped the notes on Naive Bayes, but we will come back to them.
Last week (February 4 and 6) we did most of the knn, bias-variance tradeoff notes.
We ended at the beginning of section 7.
Homework 1 is on the webpage.
Due February 21.
January 28, 2025
This would be a good time to read Chapter 2 of
"An Introduction to Statistical Learning".
Recall that there is a "with Applications in R" version
and a "with Applications in Python" version.
Note that section 2.3 of the R book is "An Introduction to R"
and section 2.3 of the python book is "An Introduction to Python".
So this would be a good place to get an R and/or python overview.
The website for the book(s) is:
https://www.statlearning.com.
Have a look at the Resources link at the top right.
Also note that you can download pdf of the books.
January 27, 2025
Folks,
I have laryngitis and cannot talk.
So, I cannot even zoom!!
Class is cancelled tommorrow.
If I get my voice back tomorrow, I will post recorded lectures.
In the meantime you should
- finish going through the Hello World in python, we stopped at about
"Get y = price and X=(mileage,year)"
If you are using python, make sure you take in the section titled "scikit-learn"
- review sections 1-7 of https://www.rob-mcculloch.org/2025_uml/webpage/notes25/NB.pdf
as soon as I can I will post recorded lectures on this stuff, we will not go over it,
I was going to do it quickly tommorrow.
Let's assume I will be able to talk on Thursday and we will start at section 8. Naive Bayes Classification.
January 24, 2025
We are at
"Get y = price and X=(mileage,year)"
in the python Hello world.
Next time we should be able to finish this up and start the Naive Bayes notes on the webpage.
January 21, 2025
Going through the Hello world in R.
Just about to do the multiple regression of y=price on x=(mileage,year).
Next time should get into the python version.
January 16, 2025
We are just going throught R Hello world:
R Hello.
We just got to Using the ggplot R-package, before Categroical variables.
You should be getting your R or python or both installs up.
Our first class is January 14, 3pm , WXLR 309:
Note that classes will be dual mode, in-person and on zoom:
Hi there,
Robert McCulloch is inviting you to a scheduled Zoom meeting.
Topic: STP494 Machine Learning
Time: Jan 14, 2025 03:00 PM Arizona
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