Learning from examples is one of the most basic aspects of human intelligence. The field of Machine Learning tries to replicate this skill and apply it to real-world problems: filtering spam from your mailbox, recognizing faces in photos, classifying documents, recommending movies and songs, or detecting credit-card fraud.
In this course you will learn the theory behind classic machine learning algorithms and at the same time get hands-on experience with using the programming language Python to apply machine learning methods to practical problems.
You will learn how to implement a typical workflow of a machine-learning project: start by analyzing your data and extracting features from your examples, then train a classification or regression model on the data, and finally evaluate how well the model makes predictions on new data.
You will learn the mechanics behind very simple learners such as the Perceptron, and learn how to implement them in Python. You will also become familiar with the Python library scikit-learn which provides easy-to-use implementations of many different classifiers and learning algorithms.
Finally, you will have the opportunity to apply machine learning techniques to realistic large data sets.
The course will consists of lectures as well as practical exercise sessions where students work on machine learning assignments.