Introduction to Supervised Machine Learning

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