Introduction to machine learning
Big Data: Technological and social changes have transformed the amount and types of data that are produced, and our capacity to process them. We have:
- Volume of data
- Variety of data
- Velocity to process it
For data to be useful, we need to process it efficiently to extract information that is useful and make decisions.
Machine learning: Type of data analysis that aims to use as much data information as possible with as little structure as needed.
Classification by type of ML task:
- Supervised learning
- Unsupervised learning
- Reinforcement learning.
Examples of ML models:
- Linear and non-linear regression
- Classification and regression trees
- Neural networks
Training
A model is trained to perform a given ML task by following the steps below:
- Data acquisition and feature selection
- Training (optimisation).
- Validation and hyper-parameter optimisation (go to 2. it needed).
- Testing.
This requires a good management of data to avoid over-fitting
An Introduction to Machine Learning in Quantitative Finance
An Introduction to Machine Learning in Quantitative Finance
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