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Artificial Intelligence (EdxArchive) 6 个评论 关注

开始时间: 04/22/2022 持续时间: 12 weeks

主页: https://www.edx.org/archive/artificial-intelligence-uc-berkeleyx-cs188-1x

简介: UC Berkeley's upper division course CS188: Introduction to Artificial Intelligence now available to everyone online.
 
"Nothing short of awesome. This is a top-notch class that teaches you a lot of important concepts in optimization and AI, while making you feel like you're on a wonderful adventure of discovery and fun." edX student review

Machine Learning (CourseraArchive) 7 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/machlearning

简介: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

Automata (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: 6 weeks

主页: https://www.coursera.org/course/automata

简介: This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness).

English Composition I: Achieving Expertise (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 12 weeks

主页: https://www.coursera.org/course/composition

简介: You will gain a foundation for college-level writing valuable for nearly any field. Students will learn how to read carefully, write effective arguments, understand the writing process, engage with others' ideas, cite accurately, and craft powerful prose. We will create a workshop environment.

Neural Networks for Machine Learning (CourseraArchive) 5 个评论 关注

开始时间: 04/22/2022 持续时间: 8 weeks

主页: https://www.coursera.org/course/neuralnets

简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

Calculus One (CourseraArchive) 4 个评论 关注

开始时间: 04/22/2022 持续时间: 14 weeks

主页: https://www.coursera.org/course/calc1

简介: Calculus One is a first introduction to differential and integral calculus, emphasizing engaging examples from everyday life.

Pre-Calculus (CourseraArchive) 0 个评论 关注

开始时间: 04/22/2022 持续时间: 8 weeks

主页: https://www.coursera.org/course/precalc

简介: Se trata de un curso pensado para facilitar la entrada del estudiante en los cursos de cálculo de primer semestre de prácticamente cualquier grado universitario, con especial énfasis en Ciencias e Ingeniería.

Calculus Two: Sequences and Series (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: 6 weeks

主页: https://www.coursera.org/course/sequence

简介: Calculus Two: Sequences and Series is an introduction to sequences, infinite series, convergence tests, and Taylor series. The course emphasizes not just getting answers, but asking the question "why is this true?"

Information Theory (CourseraArchive) 1 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

主页: https://www.coursera.org/course/informationtheory

简介: This course is an introduction to information theory, which emphasizes fundamental concepts as well as analytical techniques. Specific topics include: Information Measures, The I-Measure, Zero-Error Data Compression, Weak Typicality, Strong Typicality, Discrete Memoryless Channels, etc.

StatLearning: Statistical Learning (Stanford Online) 3 个评论 关注

开始时间: 04/22/2022 持续时间: 未知

主页: https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about

简介: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

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