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  • MIT OpenCourseWare | Free Online Course Materials
    In 2018, she discovered MIT OpenCourseWare, part of MIT Open Learning, and took her first course OpenCouseWare offers free, online, open educational resources from more than 2,500 MIT undergraduate and graduate courses
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    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
  • Get Started | MIT OpenCourseWare | Free Online Course Materials
    What is OCW? 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 Each OCW course includes a syllabus, some instructional material (such as lecture notes or a reading list), and some learning activities
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    MIT launches MITx online courses, complementing OCW’s open course materials while extending commitment to open learning OCW Educator project begins, sharing the “how” as well as the “what” of MIT education
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    This course covers the fundamentals of deep learning, including both theory and applications Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and
  • MIT Open Learning Library
    The MIT Open Learning Library is home to selected educational content from MIT OpenCourseWare and MITx courses, available for free to anyone in the world at any time **How MIT Open Learning Library Differs from MIT OpenCourseWare and MITx** You can think of OCW, MITx, and Open Learning Library along a spectrum of learning scenarios, presenting content in different formats to meet different
  • Introduction to Probability - MIT OpenCourseWare
    The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data These tools underlie important advances in many fields, from the basic sciences to engineering and management This resource is a companion site to [6 041SC Probabilistic Systems Analysis and Applied Probability] ( courses 6-041sc-probabilistic
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    This course covers elementary discrete mathematics for science and engineering, with a focus on mathematical tools and proof techniques useful in computer science Topics include logical notation, sets, relations, elementary graph theory, state machines and invariants, induction and proofs by contradiction, recurrences, asymptotic notation, elementary analysis of algorithms, elementary number
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    This course gives an introduction to analysis, and the goal is twofold: 1 To learn how to prove mathematical theorems in analysis and how to write proofs 2 To prove theorems in calculus in a rigorous way The course will start with real numbers, limits, convergence, series and continuity We will continue on with metric spaces, differentiation and Riemann integrals After that, we will move
  • Hands-On Deep Learning - MIT OpenCourseWare
    This is a fast-paced introduction to deep learning with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data Topics include the basics of deep neural networks and how to set up and train them, convolutional networks to process images and videos, transformers for natural language processing, generative large language





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