Convolution is a mathematical operation on two functions that produces a third function. It is defined as the integral of the product of the two functions after one is reflected and shifted. It has applications in many fields, such as probability, signal processing, and image processing. It also has generalizations for functions on Euclidean space and other groups.

University of Washington

Winter 2022

A general introduction to computer vision, this course covers traditional image processing techniques and newer, machine-learning based approaches. It discusses topics like filtering, edge detection, stereo, flow, and neural network architectures.

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+ 24 more conceptsStanford University

Fall 2022

This course focuses on computational techniques to study biomolecule and cell structures. Topics include molecular modeling methods, structural prediction, dynamics simulation, protein design, and computational analysis of optical microscopy images. Requires basic programming skills and biology knowledge.

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+ 21 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.

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+ 34 more conceptsUniversity of Washington

Winter 2022

This course dives deep into the role of probability in the realm of computer science, exploring applications such as algorithms, systems, data analysis, machine learning, and more. Prerequisites include CSE 311, MATH 126, and a grasp of calculus, linear algebra, set theory, and basic proof techniques. Concepts covered range from discrete probability to hypothesis testing and bootstrapping.

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+ 41 more conceptsStanford University

Spring 2022

CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.

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+ 57 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

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+ 55 more conceptsStanford University

Spring 2022

This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics.

No concepts data

+ 55 more concepts