Answer (1 of 2): 1. Reliable Distributed Algorithms, Part 1 (edX) 2. Reliable Distributed Algorithms, Part 2 (edX) by KTH University other course video available on youtube by Prof. Keshav, University of Waterloo CS 436: Distributed Computer Systems
First you need to be trained with the appropriate basis in mathematics and computer science. In the case of deep learning, you can see part 1 of the MIT Press Deep Learning book (available online for now, eventually MIT Press will have a real paper book) to either brush up on these or see which areas of math and CS are most relevant.
Course Description. This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning ...
Description: Overview of course content, including an motivating problem for each of the modules. The lecture then covers 1-D and 2-D peak finding, using this problem to point out some issues involved in designing efficient algorithms.
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization.
Leslie Kaelbling, Tomás Lozano-Pérez, Isaac Chuang, and Duane Boning. 6.036 Introduction to Machine Learning. Fall 2020. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.
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This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.