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Stochastic Estimation and Control | Aeronautics and Astronautics | MIT OpenCourseWare
are estimation
The major themes of this course are estimation and control of dynamic systems. Preliminary topics begin with reviews of probability and random variables. Next, classical and state-space descriptions of random processes and their propagation through linear systems are introduced, followed by frequency domain design of filters and compensators. From there, the Kalman filter is employed to estimate the states of dynamic systems. Concluding topics include conditions for stability of the filter equations.
Statistical Physics II | Physics | MIT OpenCourseWare
covers probability
This course covers probability distributions for classical and quantum systems. Topics include: Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Also discussed are conditions of thermodynamic equilibrium for homogenous and heterogenous systems. The course follows [8.044](/courses/8-044-statistical-physics-i-spring-2013/), Statistical Physics I, and is second in this series of undergraduate Statistical Physics courses.
Introduction to Statistical Physics | Physics | MIT OpenCourseWare
was offered
*Introduction to Statistical Physics* introduces the concepts and formalism at the foundations of statistical physics. By the end of the course, students should understand qualitative and quantitative definitions of entropy, the implications of the laws of thermodynamics, and why the Boltzmann distribution is important in modeling systems at finite temperature. In terms of skills, students should have increased their familiarity with mathematical methods in the physical science, learned how to write short programs to simulate random events, and become more adept at articulating their understanding of physics. This course was offered as part of {{% resource_link "06643bbf-f10d-4eaf-81d2-83db3472642e" "MITES Summer" %}}, a six-week, residential STEM experience for rising high school seniors. {{% resource_link "02e3fad7-cf61-4cd7-af4e-9f0069f6562b" "MIT Introduction to Technology, Engineering, and Science (MITES)" %}} provides transformative experiences that bolster confidence, create lifelong community, and build an exciting, challenging foundation in STEM for highly motivated 7th–12th grade students from diverse and underrepresented backgrounds.
Advanced Natural Language Processing | Electrical Engineering and Computer Science | MIT OpenCourseWare
is a
This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.
Deep Learning | Electrical Engineering and Computer Science | MIT OpenCourseWare
covers the
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 robotics.
Hands-On Deep Learning | Sloan School of Management | MIT OpenCourseWare
ware
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 models (such as ChatGPT), and text-to-image models (such as Midjourney). Prior familiarity with Python and fundamental machine learning concepts (such as training/validation/testing, overfitting/underfitting, and regularization) is required.
Lec 01. Introduction to Deep Learning | Deep Learning | Electrical Engineering and Computer Science | MIT OpenCourseWare
content. OCW
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