Today, Machine learning (ML) is the methodological driver behind the mega-trends of big data and data science. ML experts are highly sought after in industry and academia alike.
This course builds upon basic knowledge in math, programming and analytics/statistics as is typically gained in respective undergraduate courses of diverse engineering disciplines.
From there, it teaches the foundations of modern machine learning techniques in a way that focuses on practical applicability to real-world problems. The complete process of building a learning system is considered:
Formulating the task at hand as a learning problem;
Extracting useful features from the available data;
Choosing and parameterising a suitable learning algorithm.
Covered Topics
cross-cutting concerns like ML system design and debugging (how to get intuition into learned models and results)
feature engineering
covered algorithms include (amongst others)
Bayesian approaches
Support Vector Machines (SVM)
Neural NetworksClustering
The emerging champion of ML methods, supervised and unsupervised deep learning techniques