Business & Management (RSM)

This section provides a list of useful education resources to help you enhance your students learning experience. 

Learning from Data: Machine learning, Deep learning and AI 

For roughly a decade there has been a growing and specific interest in automated learning from digital data. Since the breakthrough of machine learning in 2012, many perspectives and conceptualizations of learning have been articulated and applied. Well-established and fine-grained learning models are now readily available for e.g., (un-)supervised, reinforcement learning, deep learning and transfer learning, to name a few. Examples of learning applications include image and speech recognition, automated driving, navigation and automated content generation.

Though learning as scientific concept originates from psychology and cognitive science research, learning from data encompasses both traditional statistical approaches, current data science methodologies as well as the latest insights in knowledge representation, cognitive science, and AI. Mastering the data skills that will enable you to (gradually) apply the learning principles in practice requires effort, perseverance and learning from others. Whether you are a novice or an expert in the field the section below provides an overview of data resources, platforms and communities that are relevant to learning from data and building your data skills.  

Data science, Machine Learning and AI

• Steve Brunton - Teaching data science and deep learning, University of Washington. 
• Machine Learning TV, Rich content, various providers. The channel is all about machine learning (ML). It contains all the useful resources which help ML learners and computer science students gain a better understanding of the concepts of this successful branch of Artificial Intelligence. (27.7K subscribers, Feb 2022)
• Michael Bronstein group, provides an integrative theory of machine learning concepts, based on geomettric algebra. Advanced material!
• Stanfordonline, Learning for a lifetime. Stanford Online is Stanford’s online learning provider, offering learners access to Stanford’s extended education and lifelong learning opportunities. A robust catalog of free and open content provides a variety of ways to expand learning, advance your career, and enhance your life. 
• MIT OpenCourseWare, OCW is a free and open online publication of material from thousands of MIT courses, covering the entire MIT curriculum, ranging from the introductory to the most advanced graduate courses.
• JHU Learning Theory, John Hopkins University (Spring 2014), basic course covering machine learning concepts     

Inspiration

• UCL Centre for Artificial Intelligence, incl. David Barber - Bayesian reasoning and machine learning [pdf], 2010. One of the best free book resources for learning data science. The core aim of the UCL's Centre for Artificial Intelligence is to create new AI technologies and advise on the use of AI in science, industry and society. The Centre brings together researchers with a shared interest in fundamental challenges in Machine Vision, Machine Learning, Natural Language Processing, Machine Action, Interpretation and Knowledge Representation.
• The Artificial Intelligence Channel. The channel is primarily focused on the future of artificial intelligence but also posts videos related to the technological singularity, transhumanism, anti-aging, synthetic biology, space exploration, technological unemployment, basic income and more, (113K subscribers, FEB22)
• Heidelberg.ai, Heidelberg.ai is a place for professionals and enthusiasts working in AI to meet and discuss. The group has regular meetups and invited talks on a variety of AI-related topics.
• Artificial Intelligence - All in One. Content related to Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (DL), Natural Language Processing (NLP), Computer Vision (CV), and special topics related to data science (131K subscribers, FEB22).
• Eye on AI, A biweekly podcast, hosted by longtime New York Times correspondent Craig S. Smith. Craig talks to people making a difference in artificial intelligence. The podcast aims to put incremental advances into a broader context and consider the global implications of the developing technology.
• Numenta, Jeff Hawkins, incl. a new fundamental theory of the human brain: "A 1000's brains of intelligence"

Data Challenges, Data Thons, etc.,

• Kaggle, write to edsc@eur.nl if you consider taking part in a challenge yourself, and find out what the EDSC experts can do for you. If you have ideas about a data thon yourself, write to edsc@eur.nl also. You can also write to rob.grim@eur.nl.

Learning from Data

Recommended MATLAB resources

Though MATLAB requires a paid license subscription, many additional software packages, modules and code examples are available free of charge to MATLAB users.The curated list below highlights a variety of freely accessible resources for students and staff to explore.    

1. Steve Brunton - Teaching data science and deep learning with MATLAB, University of Washington 
2. David Barber - Bayesian reasoning and machine learning [pdf], 2010. One of the best free book resources for learning data science with MATLAB
3. Datathon 2022 support for Women in Data Science by providing complimentary MATLAB Licenses, tutorials, and resources to each participant
4. MATLAB Central, landing page for file exchange, blogs, communities, answers and code examples   
5. The MathWorks YouTube playlists, a good starting point for discovery and learning MATLAB
6. Loren Shure - More than 30 years at MathWorks, interesting webinars on various (advanced) topics. Loren also has an informative blog worth following
7. EMPossible - Great resource for e.g., in depth understanding of working with MATLAB graphics in detail [YouTube,  website]
8. Cristopher Lum - Need to know resource for creating video and animations in MATLAB
9. Mike Fitzpatrick - Rock solid introduction to MATLAB [YouTube]
10. David Hiebeler, MATLAB/R Reference, 2015. Recommended resource for comparing MATLAB to R.