So I gave a talk at Interaction17 on what I thought designers should know about machine learning from a “how do I use this?” perspective.
Collected here are some additional notes I wish I could have covered, in three broad categories.
- Technical Foundations of Machine Learning
- Business Implications of Machine Learning
- Tools for Applying Machine Learning Today
Technical Foundations of Machine Learning
- “A Visual Introduction to Machine Learning” by Stephanie Yee & Tony Chu (yes, this is a shameless plug.) A data visualization piece that serves as an intro to decision trees, a particular type of machine learning model.
- “Machine Learning is Fun” by Adam Geitgey. A six part series introducing various applications of machine learning methods. I particularly liked it’s summary, “Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving”
- “Tensorflow Playground” from the Google Brain team. A toy example of another type of machine learning model i.e. a neural network.
- “What Every Manager Should Know About Machine Learning” by Mike Yeomans in HBR. It has a good breakdown of the concepts required for applying machine learning, e.g. feature extraction and cross validation.
Business Implication of Machine Learning
- “TensorFlow and Monetizing Intellectual Property” by Ben Thompson. The most interesting argument here is that… if Google can open source TensorFlow, it’s machine learning package, it means that Google believes it’s competitive advantage lies elsewhere. (Hint: it’s in its data.)
- “Business Implications of Machine Learning” by Drew Breunig, essentially arguing that getting training data critical mass is the new network effect. If a company tries to compete with the big’s (Google, Facebook, etc) in collecting data for ML, it will fail. To compete, companies must collect data that the big players are not collecting.