This course will provide an introduction to machine learning. It will cover two examples of machine learning algorithms: perceptrons and multi-layer perceptron. Perceptrons can be considered as the simplest possible neural networks. They are the basic units used to build multi-layer perceptrons, which are larger neural networks. After explaining these algorithms, the course will give an introduction to some of the foundations of machine learning, such as the bias-variance trade-off. Some important considerations in terms of applying machine learning algorithms will then be explained. Students are expected to be comfortable with algebra and have some basic knowledge about probability distributions and derivatives.