Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
About This Course
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.
Check back soon for how to register for our Piazza discussion board. Common questions from 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 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.
Check back soon for how to register for our Piazza discussion board. Common questions from 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.
Prerequisites
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 Methods would be more than sufficient, for example.
- Python programming required for most homework assignments.
- Recommended: At least one advanced, proof-based mathematics course
- Recommended: Computer science background up to a "data structures and algorithms" course
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