11. Introduction to Machine Learning
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Видео 11. Introduction to Machine Learning канала MIT OpenCourseWare
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson
In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Видео 11. Introduction to Machine Learning канала MIT OpenCourseWare
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
12. ClusteringMIT Introduction to Deep Learning | 6.S191Machine Learning Zero to Hero (Google I/O'19)Python Machine Learning Tutorial (Data Science)The 7 steps of machine learningMachine Learning: Living in the Age of AI | A WIRED Film12a: Neural NetsMy Journey Learning ML and AI through Self Study - Sachi Parikh - ML4ALL 20191. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)2. Optimization ProblemsDeep Learning Basics: Introduction and OverviewHow To Speak by Patrick Winston4. Stochastic ThinkingArtificial intelligence and algorithms: pros and cons | DW Documentary (AI documentary)But what is a Neural Network? | Deep learning, chapter 1A Friendly Introduction to Machine Learning5. Random WalksAndrew Ng: Artificial Intelligence is the New Electricity6. Monte Carlo Simulation