by Benjamin Villalonga Correa

# Introduction to Supervised Machine Learning

With Supervised Machine Learning techniques we can train a model to be able to recognize and classify inputs such as handritten digits, human faces, objects in a picture or sports teams with high chances of winning a game. One of the most used strategies for doing so is the use of artificial neural networks.

## Presentation Summary

In this presentation, I talk about:

- the basic problem setup for supervised machine learning.
- the classical example of MNIST.
- a motivation for using neural networks.
- some easy to code results.
- some intuition on what a neural network is actually doing.

## Examples

- Excelent tutorials for examples are given by the TensorFlow website team.
- A great (online and free) book on neural networks and deep learning is Nielsen’s textbook.

## References

- Nielsen’s book.
- Colah’s blog has good posts on several topics.
- tensorflow.org for a comprehensive library for Python with good tutorials.

### All Machine Learning.

- by Will Wei ·
**Deep Learning Partial Differential Equation (PDE)** - by Will Wei ·
**Adaptive Boosting** - by Zeqian Li ·
**Markov Decision Process and Reinforcement Learning** - by Yubo 'Paul' Yang ·
**Kriging** - by Dima Kochkov ·
**Generative Adversarial Networks** - by Xiongjie Yu ·
**Neural Network on a Tensor Train** - by Will Wheeler ·
**Reservoir Computing in the Time Domain** - by Yubo 'Paul' Yang ·
**Gibbs Sampling** - by Matt Zhang ·
**Predict Seizure with EEG** - by Matt Zhang ·
**Adaptive Boosting (AdaBoost)** - by Benjamin Villalonga Correa ·
**Introduction to Supervised Machine Learning** - by Dima Kochkov ·
**Boltzmann Machines**

Yubo "Paul" Yang ALGORITHM

artificial intelligence machine learning supervised machine learning neural networks