The prior chapter on machine learning provided a preliminary overview of the subject, including the different classes and core concepts in the subject area. This chapter will delve deeper into the theoretical aspects of machine learning such as the limits of algorithms and how different algorithms work.

Machine learning is a vast and complex subject, and to that end, this chapter focuses on the breadth of different topics, rather than the depth. The concepts are introduced at a high level and the reader may refer to other sources to further their understanding of the topics.

We will start out by discussing a few fundamental theories in machine learning, such as Gradient Descent and VC Dimension. Next, we will look at Bias and Variance, two of the most important factors in any modelling process and the concept of bias-variance trade-off.

We'll then discuss the various machine learning algorithms, their strengths and areas of applications.

We'll conclude with exercises that leverage real-world datasets to perform machine learning operations using R.

We will cover the following topics in this chapter:

• The bias, variance, and regularization properties
• Gradient descent and VC dimension theories
• Machine learning algorithms
• Tutorial: Machine learning with R