From 4a26db04189d6ec7aa99030bf2744cdc428ec5d0 Mon Sep 17 00:00:00 2001
From: "Anthony D. Blaom" <anthony.blaom@gmail.com>
Date: Fri, 2 Oct 2026 10:45:30 +1300
Subject: [PATCH 1/3] Exclude auto-update-notebooks PRs from package tests

Pull requests spawned by the generate_notebooks workflow only commit
regenerated notebook markdown, so there is nothing for the package
tests to check. Guard the job on the head branch name, mirroring the
existing condition in generate_notebooks.yml.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .github/workflows/runtests.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/runtests.yml b/.github/workflows/runtests.yml
index 51fd19b..9ee01b1 100644
--- a/.github/workflows/runtests.yml
+++ b/.github/workflows/runtests.yml
@@ -14,6 +14,8 @@ permissions:
 
 jobs:
   test_packages:
+    # exclude pull requests spawned by the generate_notebooks workflow:
+    if: ${{ !startsWith(github.head_ref, 'auto-update-notebooks') }}
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

From 3aacf488158a4062c22a37f12c77928a3dfabb83 Mon Sep 17 00:00:00 2001
From: "Anthony D. Blaom" <anthony.blaom@gmail.com>
Date: Fri, 2 Oct 2026 11:07:34 +1300
Subject: [PATCH 2/3] add the source for the "Using MLJ" slides (migrated from
 MLJ.jl/examples/)

---
 slides/README.md                              |   5 +
 .../Pumas_AI_Primary_Black_1098x398.png       | Bin 0 -> 14367 bytes
 slides/src/img/QRcodeLearnMLJ.svg             | 579 ++++++++++++++++++
 slides/src/img/machine_construct.drawio       |   1 +
 slides/src/img/machine_construct.svg          |   3 +
 slides/src/img/machine_fit.drawio             |   1 +
 slides/src/img/machine_fit.svg                |   3 +
 slides/src/img/machine_predict.drawio         |   1 +
 slides/src/img/machine_predict.svg            |   3 +
 slides/src/img/machine_save.svg               |   3 +
 slides/src/img/nested_resampling.png          | Bin 0 -> 84981 bytes
 slides/src/img/overfitting0.png               | Bin 0 -> 23522 bytes
 slides/src/img/overfitting1.png               | Bin 0 -> 45472 bytes
 slides/src/img/overfitting2.png               | Bin 0 -> 19387 bytes
 slides/src/img/overfitting3.png               | Bin 0 -> 28723 bytes
 slides/src/img/overfitting4.png               | Bin 0 -> 42056 bytes
 slides/src/img/overfitting5.png               | Bin 0 -> 45130 bytes
 slides/src/img/overfitting6.png               | Bin 0 -> 47002 bytes
 slides/src/img/overfitting7.png               | Bin 0 -> 53939 bytes
 slides/src/img/pipeline.drawio                |   1 +
 slides/src/img/pipeline.svg                   |   3 +
 slides/src/img/scitypes_small.png             | Bin 0 -> 13481 bytes
 slides/src/img/target_transformer.drawio      |   1 +
 slides/src/img/target_transformer.svg         |   3 +
 slides/src/mlj1_basics.qmd                    | 234 +++++++
 slides/src/mlj2_model_composition.qmd         | 261 ++++++++
 slides/src/mlj3_model_tuning.qmd              | 169 +++++
 slides/src/styles.css                         |  75 +++
 slides/src/theme.scss                         |  27 +
 slides/src/title-slide.html                   |  43 ++
 30 files changed, 1416 insertions(+)
 create mode 100644 slides/README.md
 create mode 100755 slides/src/figures/Pumas_AI_Primary_Black_1098x398.png
 create mode 100644 slides/src/img/QRcodeLearnMLJ.svg
 create mode 100644 slides/src/img/machine_construct.drawio
 create mode 100644 slides/src/img/machine_construct.svg
 create mode 100644 slides/src/img/machine_fit.drawio
 create mode 100644 slides/src/img/machine_fit.svg
 create mode 100644 slides/src/img/machine_predict.drawio
 create mode 100644 slides/src/img/machine_predict.svg
 create mode 100644 slides/src/img/machine_save.svg
 create mode 100644 slides/src/img/nested_resampling.png
 create mode 100755 slides/src/img/overfitting0.png
 create mode 100755 slides/src/img/overfitting1.png
 create mode 100755 slides/src/img/overfitting2.png
 create mode 100755 slides/src/img/overfitting3.png
 create mode 100755 slides/src/img/overfitting4.png
 create mode 100755 slides/src/img/overfitting5.png
 create mode 100755 slides/src/img/overfitting6.png
 create mode 100755 slides/src/img/overfitting7.png
 create mode 100644 slides/src/img/pipeline.drawio
 create mode 100644 slides/src/img/pipeline.svg
 create mode 100644 slides/src/img/scitypes_small.png
 create mode 100644 slides/src/img/target_transformer.drawio
 create mode 100644 slides/src/img/target_transformer.svg
 create mode 100644 slides/src/mlj1_basics.qmd
 create mode 100644 slides/src/mlj2_model_composition.qmd
 create mode 100644 slides/src/mlj3_model_tuning.qmd
 create mode 100755 slides/src/styles.css
 create mode 100755 slides/src/theme.scss
 create mode 100755 slides/src/title-slide.html

diff --git a/slides/README.md b/slides/README.md
new file mode 100644
index 0000000..546aa06
--- /dev/null
+++ b/slides/README.md
@@ -0,0 +1,5 @@
+# Source files for generating the "Using MLJ" slides
+
+This folder contains Quarto source only. Rendered PDF slides can be found in the relevant
+notebook source, e.g.,
+[/docs/src/notebooks/UsingMLJ/01_basics/slides.pdf](/docs/src/notebooks/UsingMLJ/01_basics/slides.pdf]).
diff --git a/slides/src/figures/Pumas_AI_Primary_Black_1098x398.png b/slides/src/figures/Pumas_AI_Primary_Black_1098x398.png
new file mode 100755
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diff --git a/slides/src/img/QRcodeLearnMLJ.svg b/slides/src/img/QRcodeLearnMLJ.svg
new file mode 100644
index 0000000..bad7e03
--- /dev/null
+++ b/slides/src/img/QRcodeLearnMLJ.svg
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diff --git a/slides/src/img/machine_predict.drawio b/slides/src/img/machine_predict.drawio
new file mode 100644
index 0000000..b39425b
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\ No newline at end of file
diff --git a/slides/src/img/machine_save.svg b/slides/src/img/machine_save.svg
new file mode 100644
index 0000000..be7aac3
--- /dev/null
+++ b/slides/src/img/machine_save.svg
@@ -0,0 +1,3 @@
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index 0000000..e2e8a23
--- /dev/null
+++ b/slides/src/img/pipeline.drawio
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new file mode 100644
index 0000000..9bebfc9
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diff --git a/slides/src/img/target_transformer.svg b/slides/src/img/target_transformer.svg
new file mode 100644
index 0000000..f41f799
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align-items: unsafe center; justify-content: unsafe center; width: 38px; height: 1px; padding-top: 287px; margin-left: 270px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; "><div style="display: inline-block; font-size: 12px; font-family: Courier New; color: #004C99; line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; word-wrap: normal; "><font style="font-size: 28px">transform<br /></font></div></div></div></foreignObject><text x="289" y="291" fill="#004C99" font-family="Courier New" font-size="12px" text-anchor="middle" font-weight="bold">transf...</text></switch></g><rect x="477.75" y="187" width="290" height="30" fill="none" stroke="none" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; 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+++ b/slides/src/mlj1_basics.qmd
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+---
+title: "Using MLJ. Lesson 1: Basics"
+author: "Anthony Blaom"
+format:
+  revealjs:
+     title: |
+       Using MLJ
+
+       Lesson 1: Basics
+     theme: [default, theme.scss]
+     margin-left: '3rem'
+     aspectratio: 32
+     navigation: horizontal
+     toc: false
+     section-titles: true
+     slide-level: 2
+     self-contained-math: true
+     auto-stretch: false
+     embed-resources: true
+     logo: figures/Pumas_AI_Primary_Black_1098x398.png
+     css: styles.css
+     slide-number: true
+     template-partials:
+         - title-slide.html
+
+---
+
+## Introducing the "Using MLJ" lessons
+
+**MLJ** is a Machine Learning toolbox written for Julia.
+
+This series of video lessons focuses on how to **use** MLJ.
+
+The lessons are not a replacement for a formal machine learning course, but can be used in
+conjunction with such a course.
+
+The lessons have been developed by **PumasAI**, and are used in the *AI for Drug
+Development Course* offered by **PumasAI** and its partner **SOPHAS**.
+
+## Introducing the "Using MLJ" lessons
+
+### Series prerequisites
+
+We assume you already know some Julia. At the very least:
+
+- You are confident interacting with Julia using the **REPL**.
+
+- You know how to install new Julia packages and how to manage Julia **package
+  environments**.
+
+There are lots of online resources for getting up to speed with Julia.
+
+Any machine learning practice depends on a solid grounding in Statistics and Linear
+Algebra.
+
+
+##  Lesson 1 Goals
+
+ We want to:
+
+1. Get to know the basic MLJ objects: **models**, **machines**
+2. Understand the basic **fit**/**predict** workflow
+3. Know how to estimate model **performance**:
+   - by hand
+   - with advanced tools (`evaluate`)
+4. Understand **scientific types** and their role in data preparation (`scitype`, `schema`)
+
+
+## Lesson 1 Prerequisites
+
+1. Familiarity with the concept of **supervised learning**
+2. Understand the distinction between **hyper-parameters** and **learned parameters** in
+   learning.
+3. Previous exposure to the idea of **cross-validation**
+
+## Getting more help
+
+The **Resources** page linked below contains:
+
+- Slides for this presentation
+- Julia code for the demos
+- Links to general MLJ learning resources
+
+![](/img/QRcodeLearnMLJ.svg){fig-align="center" width="25%"}
+
+ https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/using_mlj
+
+
+## Supervised learning
+
+You are given **hourly** temperature readings in a room:
+
+![](/img/overfitting0.png){fig-align="center" width="100%"}
+
+
+## Supervised learning {.pause}
+
+You are given **hourly** temperature readings in a room:
+
+![](/img/overfitting1.png){fig-align="center" width="100%"}
+
+. . .
+
+**Your mission:** Predict the temperature at any time. 
+
+
+## Supervised learning
+
+You try linear regression:
+
+![](/img/overfitting2.png){fig-align="center" width="100%"}
+
+
+## Supervised learning
+
+You try fitting a cubic:
+
+![](/img/overfitting3.png){fig-align="center" width="100%"}
+
+
+## Supervised learning {.pause}
+
+How about a 12th order polynomial?
+
+![](/img/overfitting4.png){fig-align="center" width="100%"}
+
+Fits all the data perfectly! 
+
+
+## Performance evaluation
+
+Lucky for me, I held back readings on the **half-hour** to test your skills.
+
+![](/img/overfitting6.png){fig-align="center" width="100%"}
+
+
+## Performance evaluation
+
+Luck for me, I held back readings on the **half-hour** to test your skills.
+
+![](/img/overfitting7.png){fig-align="center" width="100%"}
+
+Adding the lengths of the red lines, we get a **performance estimate** (mean absolute
+error).
+
+
+## What's a model? {.pause}
+
+In MLJ, a **model** is the specification of a learning algorithm and its hyper-parameters
+(specified before learning begins).
+
+**Examples:** 
+
+- Polynomial (by least squares) of degree 5.
+- A decision tree classifier with maximum depth 3. 
+- A ridge regressor with L2 regularization of 0.1.
+
+. . .
+
+A **model** does *not* include learned parameters (**fitted_params** in MLJ docs). 
+
+*Warning:* Outside of MLJ, a "model" might include learned parameters.
+
+
+## Machines 
+
+A machine starts life as a marriage of a **model** and **data**:
+
+![](/img/machine_construct.svg){fig-align="center" width="54%"}
+
+
+## Life cycle of a machine {.pause}
+
+![](/img/machine_fit.svg){fig-align="center" width="54%"}
+
+. . .
+
+*Aside:* In `fit` we can specify `rows=...` to say which `data` observations to train
+on. So `data` could be *all* available training data. Or, you might exclude a holdout test
+set.
+
+## Life cycle of a machine
+
+![](/img/machine_predict.svg){fig-align="center" width="73%"}
+
+
+## Life cycle of a machine
+
+![](/img/machine_save.svg){fig-align="center" width="73%"}
+
+## Why machines?
+
+The machine syntax anticipates an advanced MLJ feature called **learning networks**, in
+which complex MLJ workflows are "exported" as new standalone models.
+
+Learning networks are outside of the scope of this lesson.
+
+## Live coding demonstration
+
+We now demonstrate a **regression** task in which we:
+
+- Split data into `X` and `y`
+- Split observation indices into `test` and `train`
+- Choose a regression model
+- Estimate the performance by hand
+- Use a shortcut
+- Estimate using cross-validation
+
+
+## Scientific types
+
+**Machine types**: indicate how data is represented on the machine: `Float64`, `String`,
+etc.
+
+**Scitypes:** indicate how data will be *interpreted*:
+
+![](/img/scitypes_small.png){fig-align="center" width="80%"}
+
+MLJ provides:
+
+- `scitype(object)`: to see how MLJ models will interpret `object`, as it is
+  currently encoded (use `schema` instead for tables)
+
+- `coerce`: to change the machine encoding to match the desired interpretation (scitype)
+
+
+## More live coding
+
+We now demonstrate a **classification** task in which we:
+
+- Coerce data scitypes to ensure correct interpretation by MLJ
+- Horizontally and vertically split data as before
+- Demonstrate **one-hot encoding** to get `Continuous` input scitypes
+- Estimate performance by hand and with `evaluate`. 
diff --git a/slides/src/mlj2_model_composition.qmd b/slides/src/mlj2_model_composition.qmd
new file mode 100644
index 0000000..687949e
--- /dev/null
+++ b/slides/src/mlj2_model_composition.qmd
@@ -0,0 +1,261 @@
+---
+title: "Using MLJ. Lesson 2: Model Composition"
+author: "Anthony Blaom"
+from: markdown+emoji
+format:
+  revealjs:
+     title: |
+       Using MLJ
+
+       Lesson 2: Model Composition
+     theme: [default, theme.scss]
+     margin-left: '3rem'
+     aspectratio: 32
+     navigation: horizontal
+     toc: false
+     section-titles: true
+     slide-level: 2
+     self-contained-math: true
+     auto-stretch: false
+     embed-resources: true
+     logo: figures/Pumas_AI_Primary_Black_1098x398.png
+     css: styles.css
+     slide-number: true
+     template-partials:
+         - title-slide.html
+---
+
+##  Goals
+
+MLJ's design enables flexible **model composition**. Here we learn:
+
+1. What a **composite model** is
+2. How to construct model **pipelines**
+2. How composite models help us avoid **data leakage**
+3. About the **model wrapper**, `TransformedTargetModel`
+4. About other model wrappers
+5. Other kinds of model composition
+
+## Prerequisites
+
+
+1. **Lesson 1: Basics** (supervised learning,
+   machines, models, evaluation)
+2. Prior exposure to common ML pre-processing operations, such as one-hot encoding and
+   standardization
+3. Familiarity with cross-validation and the concept of **data leakage**
+
+## Getting more help
+
+The **Resources** page linked below contains:
+
+- Slides for this presentation
+- Julia code for the demos
+- Links to general MLJ learning resources
+
+![](/img/QRcodeLearnMLJ.svg){fig-align="center" width="25%"}
+
+ https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/using_mlj
+
+
+## What is a composite model?
+
+A **composite model** is a model that has other models as hyper-parameters.
+
+. . .
+
+```julia
+julia> forest = EnsembleModel(DecisionTreeClassifier())
+ProbabilisticEnsembleModel(
+  model = DecisionTreeClassifier(
+        max_depth = -1,
+        min_samples_leaf = 1,
+        min_samples_split = 2,
+        min_purity_increase = 0.0,
+        n_subfeatures = 0,
+        post_prune = false,
+        merge_purity_threshold = 1.0,
+        display_depth = 5,
+        feature_importance = :impurity,
+        rng = Random.TaskLocalRNG()),
+  atomic_weights = Float64[],
+  bagging_fraction = 0.8,
+  rng = Random.TaskLocalRNG(),
+  n = 100,
+  acceleration = CPU1{Nothing}(nothing),
+  out_of_bag_measure = Any[])
+```
+
+. . .
+
+`forest.model.max_depth` is a **nested hyper-parameter**.
+
+## Kinds of composite models in MLJ
+
+The simplest kinds of model compostion in MLJ:
+
+- **pipelines**
+- **model wrappers**
+
+Not discussed here:
+
+- A **model stack** (`Stack`) wraps multiple supervised learners
+
+- **Learning networks** are maximally flexible. Used internally to implement all the
+  above
+
+## Model pipelines
+
+![](img/pipeline.svg){fig-align="center" width="100%"}
+
+**Main point**: `pipeline` is new, standalone, supervised model, behaving like any
+other.
+
+For example, you can use `evaluate` to estimate the performance of `pipeline`.
+
+. . .
+
+### Syntax
+
+```julia
+pipeline = OneHotEncoder() |> Standardization() |> RidgeRegressor()
+```
+
+which is syntactic sugar for
+
+```julia
+pipeline = Pipeline(OneHotEncoder(), Standardizer(), RidgeRegressor())
+```
+which also allows for passing some keyword options.
+
+## Data leakage
+
+Why does the following workflow, combining standardization and ridge regression, have
+**data leakage**?
+
+**Step 1.** Standardize all the input data:
+
+```julia
+mach = machine(Standardizer(), X) |> fit!
+Xstand = transform(mach, X)
+```
+
+**Step 2.** Evaluate the performance of a ridge regressor:
+
+```julia
+evaluate(RidgeRegressor(), Xstand, y, resampling=CV(nfolds=2), measure=rms)
+```
+
+. . .
+
+
+**Answer:** Let `fold1` and `fold2` be the CV folds. When training the ridge regressor on
+`fold1`, `evaluate` is using data standardized using parameters learned from **all** the
+data, which includes `fold2`. So `fold2` is a "tainted" dataset, not appropriate for
+getting an unbiased estimate of the model's performance, which  what
+`evaluate` does to get the first CV score.
+
+
+## Data Leakage
+
+In the following workflow, training on each CV fold includes learning appropriate
+standardization parameters, but using only data from that fold. So data leakage is
+avoided:
+
+```julia
+pipeline = Standardizer() |>  RidgeRegressor()
+evaluate(pipeline, X, y, resampling=CV(nfolds=2), measure=rms)
+```
+
+. . .
+
+**Model wrappers**, discussed next, similarly help us to avoid many other sources of data
+leakage.
+
+## A model wrapper for target transformations
+
+Some supervised models perform poorly unless the **target** data is standardized.
+
+Sample task:
+
+1. Learn standarization parameters for target $y$.
+2. Apply standardization to $y$ to get $z$
+2. Train a ridge regressor using some input features $X$ and target $z$
+3. Predict on some new data $X_{\mathrm{new}}$ to obtain $\hat z$
+4. *Inverse* transform $\hat z$ to obtain $\hat y$.
+
+Notice Steps 2 and 5 both make use of the same learned standardization parameters.
+
+
+## Target transformations
+
+In code:
+
+```julia
+pstandardizer = Standardizer()
+regressor = RidgeRegressor()
+
+mach1 = machine(standardizer, y) |> fit!
+z = transform(mach1, y)
+
+mach2 = machine(regressor, X, z) |> fit!
+ẑ = predict(mach2, X)
+ŷ = inverse_transform(mach1, ẑ)
+```
+
+The fitted machine `mach1` gets used twice, in lines 10 and 14.
+
+A simple pipeline cannot replicate this workflow.
+
+## Target transformations
+
+![](img/target_transformer.svg){fig-align="center" width="65%"}
+
+## Target transformations
+
+Model wrappers to the rescue:
+
+`model = RidgeRegressor()`
+
+`wrapped_model = TransformedTargetModel(model, transformer=Standardizer())`
+
+. . .
+
+The `wrapped_model` behaves like `model`, but with target standardization automatically
+enforced internally, protecting against data leakage. 
+
+
+## Live coding
+
+We now demonstrate a supervised learning task making use of both a **pipeline** and the
+`TransformedTargetModel` **wrapper** to mitigate data leakage.
+
+## Other model wrappers in MLJ
+
+- `TunedModel(model)`: for tuning hyperparameters of `model` - see Lesson 3!
+
+. . .
+
+- `BalancedModel(model)`: to use `model` in conjunction with oversampling/undersampling
+  algorithms that correct for **class imbalance**
+
+. . .
+
+- `EnsembleModel(model)`: to create a **bagged** ensemble of `model` clones (e.g, random
+  forest)
+
+. . .
+
+
+- `IteratedModel(model)`: to wrap an iterative `model` in various iteration controls or
+  callbacks, such as **early stopping** criteria and live inspection of training losses
+
+. . .
+
+- `BinaryThresholdPredictor(model)`: for converting a probabilistic predictor into a
+  deterministic one, given a threshold probability for the "positive" outcome.
+
+. . .
+
+- `RecursiveFeatureElimination(model)`: for selecting features based on rankings of a
+  supervised `model` that reports feature importances (wrapped model is a transformer)
diff --git a/slides/src/mlj3_model_tuning.qmd b/slides/src/mlj3_model_tuning.qmd
new file mode 100644
index 0000000..573add3
--- /dev/null
+++ b/slides/src/mlj3_model_tuning.qmd
@@ -0,0 +1,169 @@
+---
+title: "Using MLJ. Lesson 3: Model Tuning"
+author: "Anthony Blaom"
+format:
+  revealjs:
+     title: |
+       Using MLJ
+
+       Lesson 3: Model Tuning
+     theme: [default, theme.scss]
+     margin-left: '3rem'
+     aspectratio: 32
+     navigation: horizontal
+     toc: false
+     section-titles: true
+     slide-level: 2
+     self-contained-math: true
+     auto-stretch: false
+     embed-resources: true
+     logo: figures/Pumas_AI_Primary_Black_1098x398.png
+     css: styles.css
+     slide-number: true
+     template-partials:
+         - title-slide.html
+
+---
+
+##  Goals
+
+Model **tuning** (or **optimization**) refers to the process of choosing the "best"
+hyper-parameters of a model. The same principles apply to choosing between *different*
+models (algorithms).
+
+Here we learn:
+
+1. How to generate a **learning curve**, a visual tool for tuning a **single** model
+   hyperparameter.
+2. How to conceptualize model tuning as a **model wrapper**.
+3. How to use the `TunedModel` wrapper in practice, to tune **multiple** parameters
+   while guarding against data leakage.
+4. How to use `TunedModel` to choose from a **list** of models, of possibly varying type.
+4. Why tuning-as-wrapper can lead to **nested resampling**.
+
+## Prerequisites
+
+1. **Lesson 1: Basics** (supervised learning, machines, models, evaluation)
+2. **Lesson 2: Model Composition** (pipelines and model wrappers)
+3. Previous exposure to random forest models helpful.
+
+## Getting more help
+
+The **resources page** below contains:
+
+- Slides for this presentation
+- Julia code for the demos
+- Links to general MLJ learning resources
+
+![](/img/QRcodeLearnMLJ.svg){fig-align="center" width="25%"}
+
+ https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/using_mlj
+
+
+## Live coding
+
+We will now demonstrate the use of MLJ **learning curves** to examine the effect of a single
+hyper-parameter.
+
+## Tuning as model wrapper
+
+We want to automate the
+process of optimizing one or more hyper-parameters.
+
+## Tuning as model wrapper
+
+In MLJ we wrap a supervised `model` in a "tuning strategy" like this:
+
+`tuned_model = `
+
+  `TunedModel(model; ranges=..., resampling=CV(), measure=..., ...)`
+
+
+. . .
+
+
+### Fitting
+
+Fitting `tuned_model` to some data does this:
+
+1. Construct a sequence of `model` mutations `model1`, `model2`, `model3`, ... , of `model`
+   with hyper-parameters varying over the specified `range`.
+
+2. Calls `evaluate` on each model mutant, using the specified `resampling` strategy and
+   `measure` to get **out-of-sample** estimates of performance
+
+3. Identifies the model with the best performance
+
+4. **Retrains** the best model **using all supplied data**.
+
+
+## Tuning as model wrapper
+
+### What does this mean for new predictions?
+
+```julia
+mach = machine(tuned_model, X, y) |> fit!
+ŷ₁ = predict(mach, Xnew)
+```
+
+```julia
+best_model = report(mach).best_model
+mach = machine(best_model, X, y) |> fit!
+ŷ₂ = predict(mach, Xnew)
+```
+
+Then `ŷ₁ == ŷ₂`.
+
+## Tuning as model wrapper
+
+You can think of `tuned_model` as a "self-tuning" counterpart of `model`.
+
+More precisely, in `tuned_model` the hyper-parameters specified by `range` have been
+transformed from **hyper-parameters** to **learned parameters**.
+
+::: {.callout-caution title="Warning"}
+
+Adding to the learned parameters **adds complexity** and whence the risk of over-fitting.
+
+:::
+
+## Live coding
+
+We now demonstrate the use of the `TunedModel` wrapper.
+
+## Nested resampling
+
+In our live coding we:
+
+**1.** Wrapped a model called `pipe` in a tuning strategy:
+
+`tuned_pipe = TunedModel(pipe, resampling=CV(nfolds=4), ...)`
+
+Here the `CV` is called **inner resampling **.
+
+**2.** Evaluated the wrapped model:
+
+`evaluate(tuned_pipe, X, y; resampling=CV(nfolds=3)`
+
+Here the `CV` is called **outer resampling **.
+
+
+## Nested resampling
+
+![](img/nested_resampling.png){fig-align="center" width="60%"}
+
+Image source: The `mlr` project.
+
+
+## Final observations
+
+- It's important to remember that a `TunedModel` is *not* a model + pre-optimized
+  hyperparameters. Hyperparameters are not optimized until training the model. In
+  particular, the optimized hyperparameters generally assume *different* values on each
+  training fold in CV estimation of the model's performance.
+
+- While the wrapper approach is the safest way to avoid common data leakage pitfalls in
+  model tuning, nested resampling can make it computationally expensive, especially when
+  used in conjunction with other model wrapping algorithms, such as `IteratedModel` or
+  `Stack`. In those cases, one may decide to "freeze" the hyper-parameters by extracting
+  `best_model` from `report(mach)`.
diff --git a/slides/src/styles.css b/slides/src/styles.css
new file mode 100755
index 0000000..02ccde5
--- /dev/null
+++ b/slides/src/styles.css
@@ -0,0 +1,75 @@
+/* ---------------------------------------------
+   Slide Logo Positioning
+--------------------------------------------- */
+.reveal .slide-logo {
+  top: 0;
+  right: 12px;
+  max-height: 1.5em;
+  position: absolute;
+}
+
+/* ---------------------------------------------
+   Base Font Settings
+--------------------------------------------- */
+.reveal div {
+  font-size: 20pt;
+  line-height: 1.6;
+}
+
+/* ---------------------------------------------
+   Headings
+--------------------------------------------- */
+.reveal h1,
+.reveal h2,
+.reveal h3,
+.reveal h4,
+.reveal h5,
+.reveal h6 {
+  font-weight: normal;
+  margin: 0.5rem 0 1rem 0.5rem;
+}
+
+/* h1: Big centered title */
+.reveal h1 {
+  font-size: 2.5rem;
+  margin-bottom: 3rem;
+  text-align: center;
+}
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+.slide-number,
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+  left: 50px;
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diff --git a/slides/src/theme.scss b/slides/src/theme.scss
new file mode 100755
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+$link-color: #51483d !default;
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+</div>
+
+</section>

From 721c24ecacdd7e228cac4ca452550a4d90f8903f Mon Sep 17 00:00:00 2001
From: "Anthony D. Blaom" <anthony.blaom@gmail.com>
Date: Fri, 2 Oct 2026 11:20:47 +1300
Subject: [PATCH 3/3] fix typos

---
 slides/src/mlj2_model_composition.qmd | 4 ++--
 typos.toml                            | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/slides/src/mlj2_model_composition.qmd b/slides/src/mlj2_model_composition.qmd
index 687949e..47cd8ac 100644
--- a/slides/src/mlj2_model_composition.qmd
+++ b/slides/src/mlj2_model_composition.qmd
@@ -92,7 +92,7 @@ ProbabilisticEnsembleModel(
 
 ## Kinds of composite models in MLJ
 
-The simplest kinds of model compostion in MLJ:
+The simplest kinds of model composition in MLJ:
 
 - **pipelines**
 - **model wrappers**
@@ -178,7 +178,7 @@ Some supervised models perform poorly unless the **target** data is standardized
 
 Sample task:
 
-1. Learn standarization parameters for target $y$.
+1. Learn standardization parameters for target $y$.
 2. Apply standardization to $y$ to get $z$
 2. Train a ridge regressor using some input features $X$ and target $z$
 3. Predict on some new data $X_{\mathrm{new}}$ to obtain $\hat z$
diff --git a/typos.toml b/typos.toml
index ad97efd..f64a1c7 100644
--- a/typos.toml
+++ b/typos.toml
@@ -1,6 +1,6 @@
 [files]
 # we exclude markdown in the notebooks, as they are auto-generated:
-extend-exclude = ["*.svg", "*.drawio", "*.html", "*.pdf", "docs/src/notebooks/**/*.md"]
+extend-exclude = ["*.js", "*.css", "*.svg", "*.drawio", "*.html", "*.pdf", "docs/src/notebooks/**/*.md"]
 
 [default.extend-words]
 # Don't correct "mape" to "map"