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+---
+title: "Exercise 1 -- ML Basics"
+subtitle: "[Introduction to Machine Learning](https://slds-lmu.github.io/i2ml/)"
+---
+
+::: {.content-hidden when-format="pdf"}
+::: {.hidden}
+{{< include ../_quarto/latex-math.qmd >}}
+:::
+:::
+
+## Exercise 1: HRO in coding frameworks
+
+Throughout the lecture, we will frequently use the `R` package 
+`mlr3`, resp. the `Python` package 
+`sklearn`, and its descendants, providing an integrated ecosystem for all 
+common machine learning tasks.
+Let's recap the HRO principle and see how it is reflected in either `mlr3` or `sklearn`.
+An overview of the most important objects and their usage, illustrated with 
+numerous examples, can be found at [the `mlr3` book](https://mlr3book.mlr-org.com/) and
+[the `scikit` documentation](https://scikit-learn.org/stable/index.html).
+
+(@) How are the key concepts (i.e., hypothesis space, risk and optimization) 
+  you learned about in the lecture videos implemented?
+  
+::: {.content-visible when-profile="solution"}
+<details> 
+<summary>**Solution**</summary>
+
+::: {.panel-tabset}
+### R
+{{< embed _r.ipynb#hro echo=true >}}
+
+### Python
+{{< embed _python.ipynb#hro echo=true >}}
+:::
+</details> 
+:::
+
+(@) Have a look at`mlr3::tsk("iris")` / `sklearn.datasets.load_iris`. What attributes does this object store?
+
+::: {.content-visible when-profile="solution"}
+<details> 
+<summary>**Solution**</summary>
+::: {.panel-tabset}
+
+### R
+{{< embed _r.ipynb#iris echo=true >}}
+
+### Python
+{{< embed _python.ipynb#iris echo=true >}}
+:::
+</details> 
+:::
+
+(@) Instantiate a regression tree learner. What are the different settings for this learner?
+  * `R` Hint: use `lrn("regr.rpart")` (`mlr3::mlr_learners$keys()` shows all available learners).
+  * `Python` Hint: use the `DecisionTreeRegressor` module and use `get_params()` to see all available settings.
+  
+::: {.content-visible when-profile="solution"}
+<details> 
+<summary>**Solution**</summary>
+::: {.panel-tabset}
+
+### R
+{{< embed _r.ipynb#learner echo=true >}}
+
+### Python
+{{< embed _python.ipynb#learner echo=true >}}
+:::
+</details> 
+:::
+
+## Exercise 2: Loss functions for regression tasks
+
+{{< include _ex_loss_functions.qmd >}}
+
+## Exercise 3: Polynomial regression
+
+{{< include _ex_polynomial.qmd >}}
+
+## Exercise 4: Predicting `abalone`
+
+{{< include _ex_abalone.qmd >}}
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+
+::: {.content-visible when-profile="solution"}
+::: {.content-visible when-format="pdf"}
+## Solution
+Lorem ipsum dolor sit amet.
+:::
+::: {.content-visible when-format="html"}
+<details>
+  <summary>Solution</summary>
+  Lorem ipsum dolor sit amet.
+</details>
+:::
+:::
+
+::: {.panel-tabset}
+## R
+
+## Python
+foo # bug: needs text between embeddings ([issue](https://github.com/quarto-dev/quarto-cli/issues/5255))
+:::
+
+::: {.content-visible when-profile="hf"}
+::: {.content-visible when-format="pdf"}
+# [Deep-dive for Major Statistics] Exercise 1$\ast$: Bar
+$$(x - 3)^2 = 5 \in \Amat, \epsm$$
+:::
+::: {.content-visible when-format="html"}
+# Exercise 1$\ast$: Bar
+$$(x - 3)^2 = 5 \in \Amat, \epsm$$
+:::
+:::
+