This chapter discussed typical financial examples and looked at machine learning towards the end. A brief introduction to the deterministic model using gross profit analysis and savings in mortgage payments was discussed.

Using real-world data in the form of options, the implied volatilities of European call options on the VSTOXX volatility index was also discussed. We also looked at Monte Carlo simulation. Using different implementation approaches, we showed simulation methods using the Monte Carlo method, the inventory problem, and a basketball situation.

Further, you learned simulation models (such as geometric Brownian and the diffusion-based simulation) with the example of the stock market model. The chapter also focused on how diffusion can be used to show drift and volatility.

We also looked at Bayesian linear regression and interactive plotting methods that one can choose from. Then, we discussed the k-nearest neighbors algorithm, instance-based learning performance, and the machine learning algorithm. This example was just touched to generate an interest about the subject and give you an idea about these algorithms. However, in the following chapter, we will look at more interesting statistical and machine learning algorithms.