New courses in Electrical Engineering and Computer Science from MIT OpenCourseWare, provider of free and open MIT course materials. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science 2022-02-11T17:22:07+05:00 MIT OpenCourseWare https://ocw.mit.edu en-US Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course is an introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. It emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-spring-2020 Spring 2020 Demaine, Erik Ku, Jason Solomon, Justin 2021-09-13T15:00:25+05:00 6.006 en-US data structure sorting hashing binary trees breadth-first search depth-first search weighted shortest path Bellman-Ford Dijkstra dynamic programming recursion complexity algorithms computational programs analysis proofs MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course is an introduction to the process of generating a symbolic description of the environment from an image. It covers the physics of image formation, image analysis, binary image processing, and filtering. Machine vision has applications in robotics and the intelligent interaction of machines with their environment. Students taking the graduate version complete additional assignments. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2020 Fall 2020 Horn, Berthold 2021-06-22T17:24:29+05:00 6.801 6.866 en-US machine vision optical flow perspective edge detection binary image processing image process gaussian image calibration shape from shading gnomonic projection brightness noise gain MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course provides a broad overview of issues related to climate change, with an emphasis on those aspects most relevant to computer scientists. Topics include climate science, climate models and simulations, decision-making under uncertainty, economics, mitigation strategies, adaptation strategies, geoengineering, policy-making, messaging, and politics.The course will culminate in a presentation of a research project which might include a paper, a blog, software etc. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s898-climate-change-seminar-fall-2019 Fall 2019 Edelman, Alan Fernandez, John Rivest, Ronald Drake, Henri 2021-05-10T19:37:47+05:00 6.S898 12.S992 en-US climate science climate change climate models decision making economics mitigation adaptation geoengineering policy politics computer science simulation MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-036-introduction-to-machine-learning-fall-2020 Fall 2020 Kaelbling, Leslie Lozano-Pérez, Tomás Chuang, Isaac Boning, Duane 2020-12-08T17:26:09+05:00 6.036 en-US machine learning algorithms modeling prediction supervised learning reinforcement learning artificial intelligence AI computer science MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 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. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s191-introduction-to-deep-learning-january-iap-2020 January IAP 2020 Amini, Alexander Soleimany, Ava 2020-12-08T17:25:59+05:00 6.S191 en-US Deep learning Computer vision Natural language processing NLP Algorithms Neural networks TensorFlow Machine learning Sequence modeling AI Artificial intelligence MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019 Spring 2019 Szolovits, Peter Sontag, David 2020-10-22T18:17:28+05:00 6.S897 HST.956 en-US machine learning healthcare AI artificial intelligence deep learning clinical data modeling medicine risk stratification diagnosis disease progression MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on learning, language, vision, event representation, commonsense reasoning, self reflection, story understanding, and analogy. It reviews visionary ideas of Turing, Minsky, and other influential thinkers and examines the implications of work on brain scanning, developmental psychology, and cognitive psychology. There is an emphasis on discussion and analysis of original papers; students taking the graduate version complete additional exercises and a substantial term project. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-803-the-human-intelligence-enterprise-spring-2019 Spring 2019 Winston, Patrick Henry 2020-05-14T15:40:32+05:00 6.803 6.833 en-US artificial intelligence human intelligence cognition emotion consciousness communication perception computational models learning language vision reasoning event representation analogy MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 6.172 is an 18-unit class that provides a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems. The course programming language is C. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-172-performance-engineering-of-software-systems-fall-2018 Fall 2018 Leiserson, Charles Shun, Julian 2019-09-23T14:50:26+05:00 6.172 en-US performance analysis algorithmic techniques high performance instruction level optimization cache optimization memory optimization parallel programming scalable distributed systems software engineering MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. It covers the topics including multilevel implementation strategies, definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lower-level elements. It also includes analysis of potential concurrency, precedence constraints and performance measures, pipelined and multidimensional systems, instruction set design issues and architectural support for contemporary software structures. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-004-computation-structures-spring-2017 Spring 2017 Terman, Chris 2019-07-12T18:32:34+05:00 6.004 en-US computation computation structure primitives, gates instructions procedures processes concurrency instruction set design software structure digital system MOS transistor logic gate combinational circuit sequential circuit finite-state machines computer architecture programming RISC processor MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This is an accelerated introduction to MATLAB® and its popular toolboxes. Lectures are interactive, with students conducting sample MATLAB problems in real time. The course includes problem-based MATLAB assignments. Students must provide their own laptop and software. This is great preparation for classes that use MATLAB. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-057-introduction-to-matlab-january-iap-2019 January IAP 2019 Celiker, Orhan 2019-07-02T11:03:29+05:00 6.057 en-US matlab simulink visualization mathworks programming curve fitting functions numerical techniques graphing equations plotting image plots surface plots debugging symbolic toolbox image processing MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This is a course on the fundamentals of probability geared towards first or second-year graduate students who are interested in a rigorous development of the subject. The course covers sample space, random variables, expectations, transforms, Bernoulli and Poisson processes, finite Markov chains, and limit theorems. There is also a number of additional topics such as: language, terminology, and key results from measure theory; interchange of limits and expectations; multivariate Gaussian distributions; and deeper understanding of conditional distributions and expectations. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-436j-fundamentals-of-probability-fall-2018 Fall 2018 Polyanskiy, Yury 2019-05-07T16:44:13+05:00 6.436J 15.085J en-US probabilistic models sample space conditional probability random variables abstract integration product measure joint distributions convolution multivariate normal distribution stochastic processes markov chains martingales MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-011-signals-systems-and-inference-spring-2018 Spring 2018 Verghese, George Oppenheim, Alan V. Hagelstein, Peter 2018-10-23T15:43:53+05:00 6.011 en-US signals and systems transform representation state-space models state observers state feedback probabilistic models random processes power spectral density hypothesis testing signal detection MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This class covers topics on the engineering of computer software and hardware systems. Topics include techniques for controlling complexity; strong modularity using client-server design, operating systems; performance, networks; naming; security and privacy; fault-tolerant systems, atomicity and coordination of concurrent activities, and recovery; impact of computer systems on society. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2018 Spring 2018 LaCurts, Katrina 2018-10-05T13:22:14+05:00 6.033 en-US computer systems systems design client server operating system networks routing atomicity network security fault tolerance authentication cryptography UNIX mapreduce databases distributed transactions DNS virtual machines DARPA traceroute RON DCTP PDP CDN BitTorrent Bitcoin VoIP LDF WAL DNSSEC DDoS Botnets Tor MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This class builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching students to program while solving puzzles. Python syntax and semantics required to understand the code are explained as needed for each puzzle. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s095-programming-for-the-puzzled-january-iap-2018 January IAP 2018 Devadas, Srini 2018-05-10T15:32:01+05:00 6.S095 en-US Programming Coding Algorithms Python programming puzzle algorithmic puzzle Merge sort Quicksort Divide and Conquer Pivot-based portioning In-place sorting Recursive function Recursive fibonacci optimization enumerative search iterative search backtracking search bipartite graphs MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016 Fall 2016 Grimson, Eric Guttag, John Bell, Ana 2017-05-18T20:40:15+05:00 6.0002 en-US Python 3.5 Python machine learning knapsack problem greedy algorithm optimization weights models computational thinking data science dynamic programming recursion knapsack problem exponential time stochastic random probability independent variables dependent variables monte carlo simulation simulation population sampling law of large numbers variance confidence interval empirical rule standard deviation central limit theorem empirical rule standard deviation bias error distribution sampling confidence interval error bars numpy scipy matplotlib pylab python plotting graphing machine learning supervised learning computer modelling signal-to-noise feature vectors classification model regression model classification classifier nearest neighbors feature scaling decision trees entropy supervised learning training data clustering cluster analysis unsupervised learning objective function dendogram statistical fallacy systematic errors correlation and causation misleading statistics GIGO axis truncating statistical fallacy extrapolation data enhancement Texas Sharpshooter Fallacy MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 6.453 Quantum Optical Communication is one of a collection of MIT classes that deals with aspects of an emerging field known as quantum information science. This course covers Quantum Optics, Single-Mode and Two-Mode Quantum Systems, Multi-Mode Quantum Systems, Nonlinear Optics, and Quantum System Theory. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-453-quantum-optical-communication-fall-2016 Fall 2016 Shapiro, Jeffrey 2017-03-09T18:55:43+05:00 6.453 en-US Quantum optics: Dirac notation quantum mechanics harmonic oscillator quantization number states, coherent states, and squeezed states radiation field quantization and quantum field propagation Prepresentation and classical fields Linear loss and linear amplification: Commutator preservation and the Uncertainty Principle beam splitters phase-insensitive and phase-sensitive amplifiers Quantum photodetection: Direct detection, heterodyne detection, and homodyne detection Second-order nonlinear optics: Phasematched interactions optical parametric amplifiers generation of squeezed states, photon-twin beams, non-classical fourth-order interference, and polarization entanglement Quantum systems theory: optimum binary detection quantum precision measurements quantum cryptography and quantum teleportation MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016 Fall 2016 Bell, Ana Grimson, Eric Guttag, John 2017-01-31T21:26:04+05:00 6.0001 en-US Computation Branching Iteration Strings Guess and check Approximations Bisection Decomposition Abstractions Functions Tuples Lists Aliasing Mutability Recursion Dictionaries Testing Debugging Exceptions Assertions Object Oriented Programming Python Classes Inheritance Program Efficiency Searching Sorting MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm 6.005 Software Construction introduces fundamental principles and techniques of software development, i.e., how to write software that is safe from bugs, easy to understand, and ready for change. The course includes problem sets and a final project. Important topics include specifications and invariants; testing; abstract data types; design patterns for object-oriented programming; concurrent programming and concurrency; and functional programming. The 6.005 website homepage from Spring 2016, along with all course materials, is available to OpenCourseWare users. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-005-software-construction-spring-2016 Spring 2016 Miller, Robert Goldman, Max 2017-01-31T21:23:39+05:00 6.005 en-US Software Construction Software Engineering Static Checking Basic Java Testing Code Review Version Control Specifications Debugging Mutability Immutability Recursion Abstract Data Types ADTs Interfaces Data Types Regular Expressions and Grammars Parser Generator Concurrency Thread Safety Networking Queues Locks Synchronization GUI Graphical User Interfaces Map filter reduce Team version control MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-881-computational-personal-genomics-making-sense-of-complete-genomes-spring-2016 Spring 2016 Kellis, Manolis 2017-01-10T19:15:22+05:00 6.881 en-US Genomes Networks Evolution computational biology genomics comparative genomics epigenomics functional genomics motifs phylogenomics personal genomics algorithms machine learning biology biological datasets proteomics sequence analysis sequence alignment genome assembly network motifs network evolution graph algorithms phylogenetics python probability statistics entropy information MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm This is a graduate-level introduction to mathematics of information theory. We will cover both classical and modern topics, including information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-441-information-theory-spring-2016 Spring 2016 Polyanskiy, Yury 2016-09-23T20:43:41+05:00 6.441 en-US properties of information entropy divergence information measures mutual information sufficient statistic mutual information probability of error entropy rate lossless data compression fixed-length compression ergodic sources universal compression binary hypothesis testing information projection channel coding achievability bounds linear codes gaussian channels input constraints lattice codes channel coding energy-per-bit source-channel separation feedback forney concatenation lossy compression distortion multiple-access channel random number generator source coding theorem noisy communication channel coding theorem source channel separation theorem broadcast channels Gaussian noise time-varying channels MIT OpenCourseWare https://ocw.mit.edu Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm