Meta-learning, also known as learning to learn, is a subset of machine learning that aims to improve the performance of learning algorithms. It does this by using the outputs and metadata from machine learning algorithms as input to optimize aspects of the learning process. This allows meta-learning algorithms to learn which machine learning algorithms work best for certain datasets and prediction tasks. They can then help reduce the number of experiments needed to find high performing models and build models that generalize well from only a few examples.

1. META-LEARNING
Meta-learning, also known as “learning to learn”, is a
subset of machine learning in computer science.

2. Agenda
• What is Meta Learning?
• Why is Meta Learning is important?
• What is the interest in meta learning?
• How does meta learning work?
• Benefits
• Reference

3. Meta-Learning
• The performance of a learning model depends on its training dataset, the algorithm, and
the parameters of the algorithm.
• Many experiments are required to find the best-performing algorithm and parameters of
the algorithm.
• Meta-learning approaches help find these and optimize the number of experiments. This
results in better predictions in a shorter time.
• Meta learning can be used for different machine learning models (e.g. few-shot
learning, reinforcement learning, natural language processing, etc.)
• Meta-learning algorithms make predictions by taking the outputs and metadata of
machine-learning algorithms as input.
• Meta-learning algorithms can learn to use the best predictions from machine-learning
algorithms to make better predictions.

4. What is Meta Learning?
Meta-learning, also known as “learning to learn”, is a subset of machine learning in
computer science. It is used to improve the results and performance of a learning
algorithm by changing some aspects of the learning algorithm based on experiment
results. Meta-learning helps researchers understand which algorithm(s) generate
the best/better predictions from datasets.
Meta-learning algorithms use metadata of learning algorithms as input. Then, they
make predictions and provide information about the performance of these learning
algorithms as output. For non-technical users, metadata is data about data. For
example, the metadata of an image in a learning model can be its size, resolution,
style, date created, and owner.
Systemic experiment design in meta-learning is the most important challenge.

5. Why is Meta Learning is important?
Machine learning algorithms have some challenges such as
• Need for large datasets for training
• High operational costs due to many trials/experiments during the training
phase
• Experiments/trials take a long time to find the best model which performs
the best for a certain dataset.
• Meta learning can help machine learning algorithms to tackle these
challenges by optimizing learning algorithms and finding learning
algorithms that perform better.

6. What is the interest in meta learning?
The interest in meta-learning has been growing during the last five
years, it has especially accelerated after 2017. As the use of deep
learning and advanced machine learning algorithms has increased, the
difficulties to train these algorithms have increased interest in meta-
learning studies.

7. How does meta learning work?
In general, a meta learning algorithm is trained with outputs (i.e. the
model’s predictions) and metadata of machine learning algorithms.
After training, its skills are tested and used to make final predictions.
Meta learning covers tasks such as-
• observing the performance of different machine learning models
about learning tasks
• learning from meta data
• performing faster learning process for new tasks

8. EXAMPLE
We may want to train a model to label
different breeds of dogs.
• We first need an annotated data set
• Various ML models are built on the
training set. They could focus just on
certain parts of the dataset
• The meta training process is used to
improve the performance of these models
• Finally, the meta training model can be
used to build a new model from a few
examples based on its experience with the
previous training process

9. Benefits
Meta learning algorithms are used to improve machine learning
solutions. The benefits of meta learning are-
• Higher model prediction accuracy
• A faster, cheaper training process
• Building more generalized models

10. Reference
• Dilmegani, C. (2022). What is Meta Learning? Techniques, Benefits &
Examples . Retrieved from AIMultiple:
https://research.aimultiple.com/meta-learning/#what-are-the-
benefits-of-meta-learning

11. Thank You