PAC learning: the framework скачать в хорошем качестве

PAC learning: the framework

The probably approximately correct (PAC) learning framework helps define the class of learnable concepts in terms of number of samples needed to achieve an approximate solutions, sample complexity, and the time and space complexity of the learning algorithm, which depends on the cost of the computational representation of the concepts. In this framework we can address fundamental questions such as: what can be learned efficiently?, what is inherently hard to learn?, how many examples do we need to learn successfully? We introduce in this video the theoretical framework of PAC learning, based on chapter 2 of the book "Foundations of Machine Learning" (2018) by M.Mohri, A.Rostamizadeh, A.Talwalkar. This video is part of a full course on Foundations of Machine Learning that is freely available at    • Welcome to the Foundations of Machine Lear...  . Coding assignments: https://github.com/ionpetre/FoundML_c...