• Tutorial: An Introduction to Learning from Programs by Marc Brockschmidt in VMCAI Winter School 2019 [slides].
• Tutorial: Modelling Natural Language, Programs, and their Intersection in NAACL HLT 2018, 1 June 2018, New Orleans, LA, USA [slides] [video]

Some resources about Big Code and Naturalness can be found at learnbigcode.github.io. A list of datasets used in this area can be found at the appendix of the survey and at learnbigcode.github.io.

A few university courses are been taught covering aspects of machine learning for code, big code or naturalnness of code. Below there are a few that have publicly available material.

• Analyzing Software using Deep Learning in T.U. Darmstadt
• Seminars on Applications of Deep Learning in Software Engineering and Programming Languages in U.C. Berkeley
• Machine learning for programming in the University of Cambridge, UK
• Deep Learning for Symbolic Reasoning in Purdue University
• Machine Learning for Software Engineering in TU Delft

Please, feel free to submit a pull request to adding more links in this page.

The last few years a few workshops have been organized in this area. Please, feel free to add any missing or future workshops here.

• ML on Code devroom at FOSDEM192-3 February 2019, Brussels, EU [videos]
• Machine Learning for Programming 18–19 July 2018, Oxford, UK [videos]
• International Workshop on Machine Learning techniques for Programming Languages 16 - 21 July 2018 Amsterdam, Netherlands
• Workshop on Machine Learning and Programming Languages in PLDI 18 - 22 June 2018, Philadelphia, PA, USA
• Workshop on NLP for Software Engineering 4 February 2018, New Orleans, LA, USA
• The 55th CREST Open Workshop - Bimodal Program Analysis 30-31 October 2017, London, UK
• Workshop on NLP for Software Engineering 13 November 2016, Seattle, WA, USA
• Programming with "Big Code" 15-18 November 2015, Dagstuhl, Germany

• CodRep: Machine Learning on Source Code Competition by KTH. Starts on April 14th 2018.

Source{d} has collected a set of links and papers in the area. You can access the list here.