Under the Hood of Machine Learning (MOOC)

In this course, we will explore Machine Learning and Neural Networks in particular. The objective is to reveal the simplicity at the core and cut through the jargon. Among other things, we will go deep into: What are Neural Networks and how are they trained? How do Neural Networks analyze images and text? How important is data? We will go under the hood to let you gain an intuition in the capabilities and constraints of this powerful toolbox.

Estimated effort: 4 hours
Requirements: It does not require any particular skills in math or coding.

• We go under the hood of what Neural Networks are, what they do and how they do it. The course digs deep into the Fully Connected Network and visualizes the representation of the inner layers.
• It goes further with a deeper intuition how such networks are trained and why it works.
• You will understand the important concepts, such as Gradient Descent, Backpropagation and different Activation Functions. Common metrics are presented as well as problems, such as overfitting and underfitting.
• Convolutional networks to analyze images are conceptually presented including the first classical LeNet-5 and AlexNet.
• An introduction is given of how words and texts are represented and analyzed through Word Embeddings and the Recurrent Neural Network, as well touching upon Attention Models.
• Interviews with some of the leading experts in Sweden are included throughout the course.
• We aim to give you the knowledge to be able to communicate with developers in ML projects and the ability to identify potential application areas within your field of expertise.
• Last but not least, we hope to build motivation for your own further learning.