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30 de set. de 2024 · MIT OpenCourseWare is an online publication of materials from over 2,500 MIT courses, freely sharing knowledge with learners and educators around the world. Learn more
This course provides an elementary introduction to probability and statistics with applications. Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
MIT OpenCourseWare is a free and open collection of material from thousands of MIT courses, covering the entire MIT curriculum. Knowledge is your reward. Use OCW to guide your own life-long learning, or to teach others.
OCW is a free and open publication of material from thousands of MIT courses across the entire MIT curriculum. That’s courses from every MIT department and degree program, and ranging from the introductory to the most advanced graduate level.
Lecture 25: Quiz 3 Review. MIT OpenCourseWare is a web based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity.
Readings. Freely sharing knowledge with learners and educators around the world. Learn more. MIT OpenCourseWare is a web based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity.
This list contains elective courses on OCW that comprise the MIT Sloan MBA first-semester core.
The participants in this seminar will dive into learning basic conversational Italian, Italian culture, and the Mediterranean diet. Each class is based on the preparation of a delicious dish and on the bite-sized acquisition of parts of the Italian language and culture.
MIT OpenCourseWare is an online publication of materials from over 2,500 MIT courses, freely sharing knowledge with learners and educators around the world. Learn more
This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.