You will at this point be familiar with different types of machine learning. We will now focus our attention on the supervised learning setting. Throughout this section you will be introduced to different supervised learning problems and familiarize yourself with different types of data. We will also look at the first concrete machine learning method: k-Nearest Neighbors (or k-NN for short). This is an intuitive method for making new predictions based on a set of training data.

These new concepts will be introduced first in the course book and then through two example tasks for you to solve. In the first example you will be asked to build a model for classifying bats and we will then look at an example problem of predicting the price of apartments.

Start by reading chapters 2.1 and 2.2 in the course book. You can then continue to the video lecture below.

I've read chapters 2.1 and 2.2 in the course book.

In this video lecture we will reiterate some of the key machine learning concepts encountered in the course book. However, we will not just look at these from a mathematical viewpoint, but also consider how we can work with these in the code. This should be very useful for solving the exercises in the upcoming section. Go ahead and watch the lecture below:

With these concepts fresh in our mind, we can now move on to our first k-NN example, where we will look at classifying bats!