Sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the entire population. It is used in statistics, quality assurance, and survey methodology to collect data more efficiently and at a lower cost than measuring the entire population. Weighting and probability theory are used to adjust for sample design, and sampling is commonly used in business and medical research. Acceptance sampling is also employed to determine if a production lot meets specifications.

Carnegie Mellon University

Fall 2020

This is an intensive course on computer graphics, covering a variety of topics such as rendering, animation, and imaging. It requires previous knowledge in vector calculus, linear algebra, and C/C++ programming. Concepts include ray tracing, radiometry, and geometric optics, among others.

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+ 24 more conceptsUC Berkeley

Fall 2022

UC Berkeley's course blends inferential thinking, computational thinking, and real-world relevance, offering students hands-on analysis of real-world datasets. It covers critical concepts in computer programming, statistical inference, privacy, and study design.

No concepts data

+ 33 more conceptsStanford University

Fall 2022

This course is an introductory one in computer graphics, focusing on synthetic computer-generated images creation. It starts with basic concepts and moves to more complex subjects like BRDF and ray tracing technology. A final project involves building a ray tracer.

No concepts data

+ 11 more conceptsUC Berkeley

Spring 2022

This course provides a broad introduction to computer graphics, covering modeling, rendering, animation, and imaging. It emphasizes the mathematical and geometric aspects of graphics and requires a data structures course and programming ability. Covered concepts range from 2D and 3D transformations to image processing.

No concepts data

+ 24 more conceptsStanford University

Winter 2023

An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.

No concepts data

+ 14 more conceptsUC Berkeley

Spring 2020

This is an introductory course to computer science theory, exploring the design and analysis of various algorithms, number theory, and complexity. The prerequisites include familiarity with mathematical induction, big-O notation, basic data structures, and programming in a standard language.

No concepts data

+ 36 more conceptsCarnegie Mellon University

Spring 2022

Similar to Course ID 29, this course provides a comprehensive introduction to computer graphics. It also demands a strong mathematical and programming background. The topics covered include rasterization, geometric transformations, and Monte Carlo ray tracing.

No concepts data

+ 22 more concepts