You join a worldwide group of learners enthusiastic to learn more and share knowledge about AI. This course focuses on three separate topics: unsupervised learning, deep learning and reinforcement learning. We start with unsupervised learning, which consists of two parts: clustering and dimensionality reduction.

Clustering is used to identify similar data/objects and patterns from your engineering datasets. It is a technique that is especially useful if you don’t have labelled or annotated data. We explain various approaches to clustering and cover how similarity and dissimilarity measures are used in clustering.  

Dimensionality reduction is used to reduce the number of features representing a given dataset, while retaining the structure of the datasets. We discuss feature selection and feature extraction techniques such as Principal Component Analysis (PCA), and how & when to apply it. 

Deep Learning is a family of machine learning methods based on artificial neural networks. You will learn how to build and train deep neural networks consisting of fully connected neural networks of multiple hidden layers. 

Reinforcement learning teaches an AI to interact with an environment. We cover basic reinforcement learning concepts and techniques, such as how to model the system using a Markov Decision Process, and how to train an optimal policy using tabular Q-learning using the Bellman equation.

For deep learning and neural networks, some prior knowledge of fundamental machine learning is necessary. For that reason, we will provide several videos from our course “Supervised Machine Learning” in this course. If you are new to machine learning, we highly recommend you watch these videos as minimal preparation. Even better preparation would be to first take this complete course before moving on to the advanced topics in this course. You can find the MOOC here, and its derived OpeCourseWare here.

We welcome you and hope you’ll learn form this course. Let’s get started!