First draft of Transfer Learning User Guide

CHANGELOG.md

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 ### Added
 - Better human readable `__str__` representation of campaign
 - README now contains an example on substance encoding results
+- Transfer Learning User Guide
 
 ### Changed
 - [WIP] `torch` is loaded lazily

docs/userguide/transfer_learning.md

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 # Transfer Learning
 
-This page will soon contain information about transfer learning.
-In the meantime, please see the [examples](../../examples/examples) instead.
+BayBE offers the possibility to mix data from multiple campaigns in order to accelerate
+optimization. 
+Using data from multiple campaigns is currently supported when using a Gaussian Process
+Surrogate model and is implemented by the [`TaskParameter`](baybe.parameters.categorical.TaskParameter).
+
+```{admonition} Terminology
+:class: note
+The term "Transfer Learning" is used in a lot of different ways.
+Within BayBE, "Transfer Learning" refers to combining data from multiple campaigns.
+Depending on the field, this might also be known as "contextual learning".
+Further, note that the act of combining the data of several contexts can be done via
+multiple models, shared architectures, special kernels in a single model, etc.
+We do not necessarily want to limit to any of these methods, even though BayBE currently
+offers only a single one.
+```
+
+## The role of `TaskParameter`
+
+The `TaskParameter` is used to "mark" the context of an individual experiment. The
+set of all possible contexts is provided upon the initialization of a `TaskParameter`
+by providing them as `values`.
+In the following example, the context might be one of several reactors in which
+a chemical experiments can be conducted.
+
+```python
+from baybe.parameters import TaskParameter
+
+TaskParameter(name="Reactor", values=["ReactorA", "ReactorB", "ReactorC"])
+```
+
+If not specified further, a campaign using the `TaskParameter` as specified above
+would now make recommendations for all possible values of the parameter. Using the
+`active_values` argument upon initialization, this behavior can be changed such that
+the `campaign` only makes recommendations for the corresponding values.
+
+The following example models a situation in which data from experiments in three
+different reactors are available, but new experiments should only be conducted in
+`ReactorC`.
+
+```python
+from baybe.parameters import TaskParameter
+
+TaskParameter(
+    name="Reactor",
+    values=["ReactorA", "ReactorB", "ReactorC"],
+    active_values=["ReactorC"],
+)
+```
+
+This can be abstracted easily to other scenarios such as changing substrates (while
+screening same reaction conditions) or formulating mixtures for different cell lines:
+
+~~~python
+TaskParameter(
+    name="Substrate",
+    values=["3,5-dimethylisoxazole", "benzo[d]isoxazole", "5-methylisoxazole"],
+    active_values=["3,5-dimethylisoxazole"],
+)
+TaskParameter(
+    name="Month",
+    values=["Liver cell", "Heart cell", "Hamster brain cell"],
+    active_values=["Liver cell"],
+)
+~~~
+
+## Seeing Transfer Learning in Action
+
+We provide full example demonstrating BayBE's transfer learning capabilities.
+We want to briefly discuss and highlight the results of this example in this user guide.
+The full example can be found [here](../../examples/Transfer_Learning/basic_transfer_learning).
+
+The example optimizes an analytical function, the so-called "Hartmann Function".
+We use transfer learning by providing additional data that was obtained by evaluating a
+negated noisy variant of the same function.
+
+The following plot demonstrates the effect that providing this additional data has:
+
+```{image} ../../examples/Transfer_Learning/basic_transfer_learning_light.svg
+:align: center
+:class: only-light
+```
+
+```{image} ../../examples/Transfer_Learning/basic_transfer_learning_dark.svg
+:align: center
+:class: only-dark
+```