Slides from my talk Fantastic Problems and Where to Find Them, first given at Futurice London's Beer & Tech session on 20/4/2017. The talk is designed as a high level introduction to machine learning and the kinds of problems it's well suited to solve. It contains real-world examples from various industries, chosen to highlight sites and services that most people will be familiar with.
There are two versions of the slideset in this repo:
- the first has more of a tech focus, and includes some case studies and discussion of the pros and cons of different programming languages for machine learning
- the second was for a generalist audience. It stresses more the importance of good design and finding a problem worth solving before applying machine learning
If you find the material here interesting and want to learn more about machine learning and related topics, there are a number of resources I find useful:
- for programmers who are beginners in the area and don't object to Python, this Github repo is an amazing collection of tutorials, blog posts, and Jupyter notebooks.
- for programmers with a bit more experience, or who prefer to use a non-Python language, this repo has a list of ML resources by language and application area.
- if you're a designer and want some ideas about how to work with systems that use machine learning, this blog post is a great starting point.
- Lassi Liikkanen has written another great article about machine learning for designers, including some suggested design patterns for interfaces backed by machine learning algorithms.
- if you just want to read more about some of the history and popular uses of machine learning, The Master Algorithm by Pedro Domingos is (afaik) the only popular science book on the topic. Luckily, it's a good read.
20/4/17 Futurice London Beer & Tech
27/6/17 Brave New World, AI Applied @ Futurice Munich
28/6/17 Futurice Munich Beer & Tech (Video)
23/10/17 Brave New World, AI Applied @ Epicenter Stockholm (Video)