Where are we and what should I be doing?

February 26, 2026

About to do KKT in the optimization notes.

Homework 2 is due March 16.
Homework 3 is due March 23.

February 12, 2026

We finished the ''KNN and the Bias-Variance'' notes
and just looked at the imports of the python code for doing KNN.

February 10, 2026

We are on slide 54 of knn.
''To predict y at the blue triangle ...''.

February 5, 2026

We are about to start section 3 ''Out-of-Sample Predictions" in the
notes on knn and the bias-variance tradeoff.

Homework 1 is due February 11.
Homework can be done in groups.

Rob office hours, Monday zoom at 7pm.

January 29, 2026

We were looking at the slide "work flow" in the sms example in the Naive Bayes notes.

Next time we will finish Naive Bayes and then start the next set of notes on
KNN and the bias-variance tradeoff.

Note the homework 1 is on the webpage.
Due February 11.
Recorded lectures for sections 9. Naive Bayes and 10. Ham or Spam are on the webpage.

January 27, 2026

We are working through the R Hello world and about to run our first regression.

Next we will do our first famous machine learning method, naive bayes.
The notes include a quick review of probability (including Bayes Theorem)
which we will skip, but recorded lectures are available on the webpage.

January 22, 2026

We are about to start the section "Let's try the variable trim" in the python Hello world.

Have a look at the scikit-learn web page (scikit-learn)
and read a bit of the documentation on the LinearRegression class.

You might also want to look at the web page for the ISL books.
ISLR.
Note that you can download pdf of the book.

There are also useful ISL labs in both R and python.
Click on Resources at the upper right corner of the webpage.
Pick ISL with R, second edition or ISL with Python.
For both R and Python you can find the labs which are at the end of each chapter of the book.
Have it a look at the lab for Chapter two, it is their introduction to R and python.
You can compare with mine!!

You can get all the ISL python labs here.
You can get al the ISL R labs here.

January 20, 2026

We got to In [82] (just before the section on Help) in the python Hello World.

Optional Reading:

Let's say that from now on we use the following to reference these 4 books:

Ra: Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
SL: An Introduction to Statistical Learning: with Applications in Python (or R)
MLR: Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models
HO: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition

For more information on the books see books

Ra: Chapter 1
SL: Chapters 1 and 2
MLR: Chapter 1 is an overview of Machine Learning and Chapter 2 is an overview of R.
HO: Chapter 1.

January 15, 2026

On Thursday, January 15, we went the the syllabus and course info.
We are about to look at the Hello world in python (Hello python).

No homework yet, just think about what software you want to use
{e.g. install R/rstudio or anaconda).

January 13, 2026

Folks,

I have had some health issues so the first class (January 13) is cancelled.
I'm hoping to zoom on Thursday January 15 and then make it in person the following week (January 20,22).

Note that the in-person classes will also be available via zoom and recordings of all classes will be available.

All class materials are available at:
https://www.rob-mcculloch.org/2026_gml/webpage/index.html

Please check out the webpage.  In particular note the link
Where are we and what I should be doing?
https://www.rob-mcculloch.org/2026_gml/webpage/ww.html

Basic computational skills are needed for the class.
I will support R and python, but you can use other software (e.g. matlib) if you want.

For python please check out my notes at:
https://www.rob-mcculloch.org/python/index.html.
If you don't have a python/data science installed on your machine you
might want to try installing anaconda as discussed on the webpage.

Please also have a look at
https://www.rob-mcculloch.org/python/Py_Hello-World_Regression.html (at bottom of the webpage above).
This is my basic intro to data science/scikit-learn.
If you don't know python, I think it is feasible to learn it as we go along
but note my suggested books at:
https://www.rob-mcculloch.org/books.txt.

For R please check out my notes at:
https://www.rob-mcculloch.org/R/index.html.
If you don't have a R/rstudio installed on your machine you should do so.
Also have a look at https://www.rob-mcculloch.org/R/R_Hello-World_Regression.html.
This is my basic introduction to R.
If you don't know R, I think it is feasible to learn it as we go along
but note my suggested books at:
https://www.rob-mcculloch.org/books.txt.

January 12, 2026

Hi there, 

Robert McCulloch is inviting you to a scheduled Zoom meeting. 

Topic: STP 550, Statistical Machine Learning
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January 1, 2026

Nothing to be done at this time.