Introduction to Neural Networks

1. Neural networks
2. A simple neural network classifier
3. Image kernel convolution
4. Convolutional neural networks
5. Applying CNNs
6. Data augmentation
7. Ethics of machine learning
8. Summary

What is machine learning?

• Machine learning (ML) is a technique which uses computers to discover patterns or information about your data.
• It is a part of the wider field of artificial intelligence
• There are lots of different types of machine learning

Examples of machine learning

• Simplest ML algorithm could be a linear regression. It automatically and iteratively looks at your data to calculate the parameters of your $y = mx + c$ curve
• A more advanced technique is K-means clustering. It is a way of finding clusters of points in your data without having to input any explicit labels.
• The most famous is neural networks (NN) which were inspired by the brain and use a directed network of connected neurons to describe features of the data set.
  • More recently (since about 2010) deep neural networks (DNN) have become possible, allowing more detailed models of data to be learned starting the modern buzz for deep learning.

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