While preparing for a seminar of the EESTech Challenge (official site, EESTEC news), it required a while to find some reliable sources for the history of Machine Learning. Even though Machine Learning has become a buzzword recently, there are surprisingly scarce material on its history, so I will share these references here accompanied with few comments.

The references are summarised in an increasing length and scientific depth manner, i.e. if you have more time, you can read the ones towards the end. This non-exhaustive list is the following:

• BBC timeline: The development of AI through 15 discrete milestones over the last 70 years. Focus: commercial, political evolution of the subject, it includes links to other BBC articles for each milestone.
• History-extra: The progress in AI divided into 7 eras, starting from ancient Greece. It provides a very brief introduction of the pre-electronic machinery intelligence, while it includes only high level progress of the AI over the last 70 years, mainly through the AI funding decisions.
• History of discriminative learning: A brief introduction to supervised discriminative methods written from an engineering perspective. The author recaps the most popular discriminative techniques, like Support Vector Machines, along with the related scientific articles. It also includes few references to the recent progress in the field of Neural Networks.
• Paper on supervised methods: The history of supervised methods for machine learning. The authors of the paper (Machine learning: a review of classification and combining techniques) provide a more detailed overview of the supervised methods. This article is few years old, so it does not reflect on any recent developments, however it provides a decent introduction to the majority of the methods till then.
• Notes on AI history: The focus is on classic AI techniques, through the performance of AI into games. It includes also a reference to the ALPAC and the Lighthill report that included pessimistic predictions about the (short-term) financial returns and the small-scale success of AI.
• The Quest of Artificial Intelligence: A lengthy book, available from Cambridge University Press, by Nils Nilsson, one of the early contributors of machine learning. The book is quite detailed, it develops the techniques in a chronological order along with several more philosophical questions that led to this development. It also includes (part of ) the motivation for developing the techniques, e.g. how probabilistic reasoning was not included in the early approaches to AI and how the need for probabilistic methods emerged. Definitely recommended if you want a more thorough understanding of the historic portrait of machine learning evolution.

If you happen to know additional links or references, I would be happy to add them to the list.