6 Supervised Learning

<Under construction. Add chapter intro.>

6.1 Cognitive experiment: Rule induction

<Under construction: Experiment 1 and Experiment 2.>

<Under construction: Confirmation bias.>

Exercise 3b

How could this activity be viewed as search? Hint: There are multiple search spaces!

Solution to Exercise 3b

6.3 Cognitive experiment: Rule induction from a fixed set of examples

In the prior experiments, the reasoning agent was simultaneously searching for items xi in X to be labeled, as well as functions fi in F to consider as candidates for the target rule. Next, we consider a simplified variant of the rule induction task in which we assume a fixed set of labeled examples. This task is based on a clever brainteaser.

<Under construction. Add puzzle here. Also add hidden hint.>

6.4 Standard supervised learning

This experiment was different from the earlier ones because you just had one search space to contend with: searching for candidate functions fi in F. The set of examples you had to work with, Tknown in T, was fixed. In other words, the specific search problem was:

Given Tknown in T, select one candidate function fi in F that best explains Tknown.

It turns out that this exact problem is what is called supervised learning in AI. Supervised learning is one type of machine learning, and probably currently the biggest sub-area of machine learning in terms of real-world applications.

The reason supervised learning is so powerful and widely used is that, if you have good techniques for doing this particular type of search (which we do have!), you can take lots of very different kinds of real world problems and write them as supervised learning problems. For example:

<Under construction: Examples.>

There are also other, more unfortunate examples of things that can be (and in fact have been) defined as supervised learning problems, such as:

<Negative examples.>

6.5 Core technical issues in supervised learning

  • Evaluation

  • Hypothesis space and search algorithm

  • Features

6.6 Evaluating supervised learning solutions

6.7 The hypothesis space

6.8 The search algorithm

6.9 Features

6.10 Supervised learning variants

  • deep learning

  • active learning

  • semi-supervised learning

  • self-supervised learning

Page built: 2022-07-28 using R version 4.1.1 (2021-08-10)

Please cite as:   Kunda, M. (2022). Triangle AI Book. https://www.triangleaibook.org
View source
Website analytics provided by Plausible.io, a deliberate choice made to preserve your privacy. (See here for more on the rationale behind this choice, and the role of AI in modern surveillance.)
Creative Commons License   This work is licensed under the Creative Commons BY-NC-ND 4.0 License. This means you are welcome to redistribute material from this book but only: (1) WITH attribution, (2) for NON-commercial purposes, and (3) WITHOUT modifications, in order to preserve the intellectual integrity of this work.