Machine learning is an umbrella term for solving problems that would be too costly to develop algorithms for by human programmers, using supervised, unsupervised and reinforcement learning. It has applications in computer vision, speech recognition, email filtering, agriculture and medicine. ML is also known as predictive analytics and its mathematical foundations are provided by mathematical optimization methods.
UC Berkeley
Spring 2020
The course addresses programming parallel computers to solve complex scientific and engineering problems. It covers an array of parallelization strategies for numerical simulation, data analysis, and machine learning, and provides experience with popular parallel programming tools.
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+ 36 more conceptsUniversity of Washington
Autumn 2019
A survey course on neural network implementation and applications, including image processing, classification, detection, and segmentation. The course also covers semantic understanding, translation, and question-answering applications. It's ideal for those with a background in Machine Learning, Neural Networks, Optimization, and CNNs.
No concepts data
+ 13 more conceptsUniversity of Washington
Summer 2022
This course offers an intermediate level of data programming, focusing on different data types, data science tools, code complexity, and memory management. It emphasizes the efficient use of concepts for data programming.
No concepts data
+ 34 more conceptsStanford University
Autumn 2022-2023
Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.
No concepts data
+ 88 more conceptsBrown University
Fall 2022
CSCI 0112 progresses from CSCI 0111, focusing on structuring programs for solving isolated subproblems. It delves into various algorithms, implementations from abstract descriptions, data organization methods, and program efficiency. Ethical considerations in software development are underscored. Topics include data structures, OOP, web APIs, machine learning, and more.
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+ 25 more concepts