Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals.
The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist. The course includes a complete set of homework assignments, each containing a theoretical element and implementation challenge with support code in Python, which is rapidly becoming the prevailing programming language for data science and machine learning in both academia and industry. This course also serves as a foundation on which more specialized courses and further independent study can build.
Please fill out this short online form to register for access to our course's Piazza discussion board. Applications are processed manually, so please be patient. You should receive an email directly from Piazza when you are registered. Common questions from this and previous editions of the course are posted in our FAQ.
The first lecture, Black Box Machine Learning, gives a quick start introduction to practical machine learning and only requires familiarity with basic programming concepts.
The quickest way to see if the mathematics level of the course is for you is to take a look at this mathematics assessment, which is a preview of some of the math concepts that show up in the first part of the course.
Solid mathematical background, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate differential calculus, probability theory, and statistics. The content of NYU's DS-GA-1002: Statistical and Mathematical Methodswould be more than sufficient, for example.
Python programming requiredfor most homework assignments.
Recommended:At least one advanced, proof-based mathematics course
Recommended:Computer science background up to a "data structures and algorithms" course
One of the best courses on the foundations of machine learning that I've encountered anywhere. This course repeatedly goes beyond the simplistic and occasionally misleading explanations provided by other high-profile introductory courses. For example, the discussion of the geometric interpretation of regularisation in terms of isocontours in parameter space goes a level deeper than the equivalent account offered in other courses, providing real insight and intuition. That's typical of this course. The lecturer does a great job of elucidating the subtleties of the subject, assuming very little prior knowledge.