This is awesome!

Often I have opened programming book and right off I'm in the unknown, Now it seems I can start with understanding. Then work towards the parts. There is a lot of parts within ML and statistics so the learning does not stop here. A broad overview seems to be a good start.

Machine Learning vs Statistics. What is the difference between both? That's something I learned right away. That's an way to start understanding without doing any math work myself. That's something I am trying to change my paradigm of thought. With Mathematica you do not need to do the math yourself. So, that is a different way of learning compared to step-by-step ladder. I have to be careful, as Mathematica shows with levels in many places. I can go to a level of understanding or math work. Am I understanding the same at levels? Maybe, a level can look the same to another. I think of the example of thinking of a concept or understanding, zoomed in or zoomed out. Zoom in enough and the picture does not look like anything at a different level.

ML vs Statistics what is a level above both? "Data science" I propose that you could call that a level above both. The process of getting data, then wrangling it. Turning it into something meaningful. Is it to make predictions? ML claims to be the sole purpose for. Is it to make sense of the data? Statistics is the purpose to make sense of the data with logical order or programmatically. So with those definitions you can say that "Data Science" is a level above both. As the AI is a level above everything. The models part of the AI or a level below somewhere. Understanding the where/what you want to do can help work towards a goal of where you work with either ML and statistics and so on. I have never finished a formal class of statistics myself. This definition helps me understand what statistics or ML is.

Here's a question for experienced people in statistics, Would you agree or disagree that statistics is not to make predictions?

I think statistics is a mathematical language for describing and modeling things. What you use those descriptions and models for, that's engineering and science. Sometimes rhetoric and propaganda. It is sometimes said there are "liars", "damned liars", and "statisticians" . It lets you be wrong with confidence. And yes, ChatGPT does tell lies. On the other hand, if statistics weren't so effective at making truthful descriptions, you would not be able to read these words on paper nor take effective medications.

Sorry about that. For these issues I think it would be better to run the code in the notebooks that contain just the code from here https://www.wolfram.com/language/introduction-machine-learning/inc/Bernard-MachineLearning-Code-NotebookEdition.zip The notebooks containing the entire lesson (text and code) seem to have the issue you have highlighted in your screenshot!

Hi

It looks like we run in to similar issues with tomorrow's session on Clustering.

For example, the News Aggregator example returns errors, please see below,

Russell.

topics = {"Tennis", "SpaceX", "COVID-19 pandemic"};
pages = WikipediaData /@ topics;

articles = Partition[TextSentences[#], 10] & /@ articles;
articles = Map[StringRiffle, articles, {2}]; articles = 
  Flatten[Thread /@ Thread[articles -> topics]]; articletopics = Values[articles];
articles = Keys[articles];
Length[articles]

https://www.eia.gov/petroleum/weekly/

There's some data here that I can use, I have not attempted to use it. This is a challenge goal for me before I spend time wrangling the data, what ML predictions would I want to look for?

Should we use a Classify or Regression model of projected fuel prices for the next 5 years (more relevant to us because knowing this will help us prepare for the prices to either increase or fluctuate). Do we want to look further out 10 years? 20 years?

Today's lecture would suggest Regression since it's a numerical model that we are looking at. Giving us a price of fuel.

What ML methodologies wold we approach making a model trying to predict future gas prices? This data goes back up to 1980's for some of it.

Another question, are the fuel prices histrionically in line with inflation? Would we want to look for comparison data and make another ML model or make a simple assumption on the percent of inflation?

As with any tool, there are classes of problems that benefit from machine learning and other AI tools. For example automated image recognition of manufacturing defects, or Star Trek's universal translator. (That's got to be right around the corner: Put an earbud in, and hear a person speak in your own language while cancelling the sound of their native language.)

But for classes of problems that already have robust solutions, maybe not so much. Just a quick review of some literature on error correcting codes. It's a pretty advanced field. Folks using AI generated error correcting CODECs were, at least 4 or 5 years ago, getting similar results with both techniques.

Comparing results between methods would be good at providing insight into how deep learning is actually solving problems, and may even provide insight into how organic neuro-nets actually work. Someday we may be (bio)engineering those as well.

I see that now. Here is the cleaned up code that should work:

Show[Plot[
  QuantityMagnitude@PDF[dist, Quantity[x, "Feet"]], {x, 58, 88}, 
  Filling -> Bottom, PlotRange -> {{0, 140}, {0, Automatic}}, 
  PlotStyle -> Gray, Frame -> {True, True, False, False}, 
  PlotLabel -> "Predictive distribution for 23 mi/h", 
  PlotLegends -> SwatchLegend[{"68% probability interval"}], 
  FrameLabel -> {"distance (ft)", "probablity density"}, 
  ImageSize -> 360], 
 Plot[QuantityMagnitude@PDF[dist, Quantity[x, "Feet"]], {x, 0, 140}, 
  PlotLegends -> {"probability density"}, Filling -> Bottom, 
  PlotRange -> {0, Automatic}]]

Hi Abrita, I have a quick question: what model could I use to identify : How much does a variable contribute to the classification?. My predictor variable is binary and I have 11 explanatory numerical variables. Thanks

You can use SHAPValues to figure out the contribution of a feature. It is available as a n option for both PredictorFunction and ClassifierFunction.

 c = 
 Classify[{<|"Age" -> 32, "Height" -> 160|> -> 
    "Female", <|"Height" -> 183, "Age" -> 41|> -> 
    "Male", <|"Height" -> 123|> -> 
    "Female", <|"Height" -> 175, "Age" -> 21|> -> 
    "Male", <|"Age" -> 11|> -> 
    "Male", <|"Age" -> 52, "Height" -> 164|> -> "Female"}]

In[14]:= c[<|"Age" -> 12, "Height" -> 120|>, "SHAPValues"]
Out[14]= <|"Female" -> <|"Age" -> 0.915746, "Height" -> 26.9166|>, 
 "Male" -> <|"Age" -> 1.09201, "Height" -> 0.0371519|>|>

The way SHAP values are computed is basically to see how much results change if different features in the data are dropped.

To save the "unsaveable" notebook with your edits, select from the menu Format-> Options inspector and then with "Selected Notebook" selected in the drop down at top left, search for the "saveable" and set it to True.

Additional material discussed during the session on clustering today https://www.wolframcloud.com/obj/online-courses/multiparadigm-data-science/cluster-analysis.html

That was what my professor said in my very first class on machine learning :)

Hi, regarding the Clustering chapter, I got most of it but have been puzzling over the use of the NotablePerson model to extract probablilities to feed into (I think) the KL Divergence parameters used in the FindClusters command.

It just seems to come out of nowhere, and is not much-explained in the text.

Is this something we can draw some resuable lessons from?

Thanks

How many possible classes do you expect to have? If each one-hot encoded vector is a possible class then it seems like many more classes than samples. Would it be possible to aggregate the classes into fewer "super-classes"?

Also your dataset has only two input variables right? You are definitely welcome to use a Neural Net model, but perhaps Predict would be easier to use with its automated approach to feature extraction, and access to multiple other methods.