MarketingOpen learning
Adaptive Markets: Financial Market Dynamics and Human Behavior
Economists can’t agree on whether investors and markets are rational and efficient, as modern financial theory assumes, or irrational and inefficient, as behavioral economists believe. Drawing on psychology, evolutionary biology, neuroscience, artificial intelligence, and other fields, Prof. Lo cuts through the debate in this course with a new framework—the Adaptive Markets Hypothesis—in which rationality and irrationality coexist. Topics: Introduction and Financial Orthodoxy Rejecting the Random Walk and Efficient Markets Behavioral Biases and Psychology The Neuroscience of Decision-Making Evolution and the Origin of Behavior The Adaptive Markets Hypothesis Hedge Funds: The Galapagos Islands of Finance Applications of Adaptive Markets The Financial Crisis Ethics and Adaptive Markets The Finance of the Future and the Future of Finance As part of the Open Learning Library (OLL), this course is free to use. You have the option to sign up and enroll if you want to track your progress, or you can view and use all the materials without enrolling. Resources on OLL allow learners to learn at their own pace while receiving immediate feedback through interactive content and exercises.
Dept 1515.481X+fall_2022
HumanitiesOpen learning
Advanced Natural Language Processing
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.
Dept 66.864+fall_2005
Computer ScienceOpen learning
Adventures in Advanced Symbolic Programming
This course covers concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Substantial weekly programming assignments are an integral part of the subject. There will be extensive programming assignments, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or some other “functional” language.
Dept 66.945+spring_2009
EngineeringOpen learning
AI 101
Machine vision. Data wrangling. Reinforcement learning. What do these terms even mean? In AI 101, MIT researcher Brandon Leshchinskiy offers an introduction to artificial intelligence that’s designed specifically for those with little to no background in the subject. The workshop starts with a summary of key concepts in AI, followed by an interactive exercise where participants train their own algorithm. Finally, it closes with a summary of key takeaways and Q/A. All are welcome!
Dept 6RES.6-013+fall_2021
MathematicsOpen learning
Algorithms for Inference
This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.
Dept 66.438+fall_2014
Computer ScienceOpen learning
Artificial Intelligence
This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.
Dept 66.034+spring_2005
Computer ScienceOpen learning
Artificial Intelligence
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Dept 66.034+fall_2010
MathematicsOpen learning
Brain and Cognitive Sciences Computational Tutorial Series
This is a seminar series led by graduate students and postdocs in the MIT Department of Brain and Cognitive Sciences (BCS) from 2015 to the present, featuring tutorials on computational topics relevant to research on intelligence in neuroscience, cognitive science, and artificial intelligence. These tutorials are aimed at participants who have some computational background but are not experts on these topics. A computational tutorial can consist of any method, tool, or model that is broadly relevant within neuroscience, cognitive science, and artificial intelligence. The goal is to bring researchers in brain and cognitive sciences closer to the researchers creating computational methods. Resources posted here include lecture videos, lecture slides, code and datasets for exercises, background references, and other supplementary material. Typically, each tutorial consists of a short lecture, and an interactive part with tutorials or “office hours” to work through practice problems and discuss how the material may be applied to participants’ research. This series was organized by Emily Mackevicius, Jenelle Feather, Nhat Le, Fernanda De La Torre Romo, and Greta Tuckute, with financial support from BCS. Videos were filmed, edited, and produced by Kris Brewer, Director of Technology at the Center for Brains, Minds, and Machines (CBMM).
Dept 9RES.9-008+fall_2023
Computer ScienceOpen learning
Brains, Minds and Machines Summer Course
This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Materials are drawn from the Brains, Minds and Machines Summer Course offered annually at the Marine Biological Laboratory in Woods Hole, MA, taught by faculty affiliated with the Center for Brains, Minds and Machines headquartered at MIT. Elements of the summer course are integrated into the MIT course, 9.523 Aspects of a Computational Theory of Intelligence. Contributors This course includes the contributions of many instructors, guest speakers, and a team of iCub researchers. See the complete list of contributors.
Dept 9RES.9-003+summer_2015
EnergyOpen learning
Brave New Planet
Utopia or dystopia? It’s up to us. In the 21st century, powerful technologies have been appearing at a breathtaking pace—related to the internet, artificial intelligence, genetic engineering, and more. They have amazing potential upsides, but we can’t ignore the serious risks that come with them. Brave New Planet is a podcast that delves deep into the most exciting and challenging scientific frontiers, helping us understand them and grapple with their implications. Dr. Eric Lander, president and founding director of the Broad Institute of MIT and Harvard, is a geneticist, molecular biologist, and mathematician who was a leader of the Human Genome Project and for eight years served as a science advisor to the White House for President Obama. He’s also the host of Brave New Planet , and he’s talked to leading researchers, journalists, doctors, policy makers, activists, and legal experts to illuminate how this generation’s choices will shape the future as never before. Brave New Planet is a partnership between the Broad Institute, Pushkin Industries, and the Boston Globe.
Dept 7RES.7-003+fall_2020
Machine LearningOpen learning
Collaborative Data Science for Healthcare
This course provides an introductory survey of data science tools in healthcare. It was created by members of MIT Critical Data, a global consortium consisting of healthcare practitioners, computer scientists, and engineers from academia, industry, and government, that seeks to place data and research at the front and center of healthcare operations. The most daunting global health issues right now are the result of interconnected crises. In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers, and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care. What you’ll learn: Principles of data science as applied to health Analysis of electronic health records Artificial intelligence and machine learning in healthcare 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.
Dept HSTHST.953+fall_2020
Systems EngineeringOpen learning
Computational Cognitive Science
This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?
Dept 9, 69.66J+fall_2004
EngineeringOpen learning
Computational Cognitive Science
An introduction to computational theories of human cognition. Emphasizes questions of inductive learning and inference, and the representation of knowledge. Project required for graduate credit. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.
Dept 99.52-C+spring_2003
LinguisticsOpen learning
Computational Models of Discourse
This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing. The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing. This subject qualifies as an Artificial Intelligence and Applications concentration subject.
Dept 66.892+spring_2004
HistoryOpen learning
Cultures of Computing
This course examines computers anthropologically, as artifacts revealing the social orders and cultural practices that create them. Students read classic texts in computer science along with cultural analyses of computing history and contemporary configurations. It explores the history of automata, automation and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; robots, cyborgs, and artificial life; creation and commoditization of the personal computer; the growth of the Internet as a military, academic, and commercial project; hackers and gamers; technobodies and virtual sociality. Emphasis is placed on how ideas about gender and other social differences shape labor practices, models of cognition, hacking culture, and social media.
Dept 21A, STS, WGS21A.350J+fall_2011
SociologyOpen learning
Current Debates in Media
This class addresses important, current debates in media with in-depth discussion of popular perceptions and policy implications. Students will engage in the critical study of the economic, political, social, and cultural significance of media, and learn to identify, analyze, and understand the complex relations among media texts, policies, institutions, industries, and infrastructures. This class offers the opportunity to discuss, in stimulating and challenging ways, topics such as ideology, propaganda, net neutrality, big data, digital hacktivism, digital rebellion, media violence, gamification, collective intelligence, participatory culture, intellectual property, artificial intelligence, etc., from historical, transcultural, and multiple methodological perspectives.
Dept CMS-WCMS.701+spring_2015
Digital Business & ITOpen learning
Data Mining
Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.
Dept 1515.062+spring_2003
Pedagogy and CurriculumOpen learning
Day of AI
This resource is to support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher training, all of which is available at no cost to participants. Developed by leading faculty and educators from MIT RAISE, the curriculum features up to four hours of hands-on activities that engage kids in creative discovery, discussion, and play as they learn the fundamentals of AI, investigate the societal impact of these technologies, and bring artificial intelligence to life through lessons and activities that are accessible to all, even those with no computer science or technical background.
Dept MASRES.MAS-002+spring_2022
HumanitiesOpen learning
Ethics for Engineers: Artificial Intelligence
Artificial Intelligence (AI), and the algorithmic judgment at its core, is developing at breakneck speed. This version of the popular Ethics for Engineers course focuses on the ethics issues involved in the latest developments of computer science.
Dept 16, 10, 1, 6, 2, 2210.01+spring_2020
Data Science, Analytics & Computer TechnologyOpen learning
Ethics of AI Bias
This video aims to delve into the human problems brought out by issues in artificial intelligence, specifically with respect to bias. It is suitable for classroom use or as a standalone video for those who wish to understand the issue more deeply than is conventionally covered. For classroom use, we recommend watching the chapterized version of the video and working through the teaching materials provided for each chapter.
Dept 10RES.10-002+spring_2023
Policy and AdministrationOpen learning
Ethics of Technology
This course introduces the tools of philosophical ethics through application to contemporary issues concerning technology. It takes up current debates on topics such as privacy and surveillance, algorithmic bias, the promise and peril of artificial intelligence, automation and the future of work, and threats to democracy in the digital age from the perspective of users, practitioners, and regulatory/governing bodies.
Dept 2424.131+spring_2023
AIOpen learning
Foundation Models and Generative AI
ChatGPT, Copilot, CLIP, Dall-E, Stable-Diffusion, AlphaFold, self-driving cars—is now the time that artificial intelligence (AI) lives up to all its hype? What’s the secret sauce behind these recent breakthroughs within AI? They’re called foundation models and generative AI, and it is changing everything. With the help of it, some believe that artificial general intelligence (AGI) has already been achieved. In this non-technical series of lectures, we will start with a short history of AI, then move on to with what supervised learning and reinforcement learning is missing, and conclude with the deep practical and foundational implications foundation models and how we arrive at them via self-supervised learning. We cover applications in both science and business. All backgrounds are welcome.
Dept 66.S087+january-iap_2024
HumanitiesOpen learning
Foundations of Cognition
Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project is required for graduate credit.
Dept 99.69+spring_2003
Educational TechnologyOpen learning
Generative Artificial Intelligence in K–12 Education
The emergence of transformer architectures in 2017 triggered a breakthrough in machine learning that today lets anyone create computer-generated essays, stories, pictures, music, videos, and programs from high-level prompts in natural language, all without the need to code. That has stimulated fervent discussion among educators about the implications of generative AI systems for curricula and teaching methods across a broad range of subjects. It has also raised questions of how to understand both these systems and the at times overstated claims made for them. This class will introduce the foundations of generative AI technology, and participants will explore new opportunities it enables for K–12 education. It will also describe and explore how an analytical frame of mind can help make clear the core issues underlying both the successes and failures of these systems. Much of the work will be project-based, involving implementing innovative teaching and learning tools and testing these with K–12 students and teachers.
Dept 6, MAS6.S062+fall_2023
Data Science, Analytics & Computer TechnologyOpen learning
How to AI (Almost) Anything
Artificial Intelligence (AI) holds great promise as a technology to enhance digital productivity, physical interactions, overall well-being, and the human experience. To enable the true impact of AI, these systems will need to be grounded in real-world data modalities, from language-only systems to vision, audio, sensors, medical data, music, art, smell, and taste. This course will introduce the basic principles of AI (focusing on modern deep learning and foundation models) and how we can apply AI to novel real-world data modalities. In addition, we will introduce the principles of multimodal AI that can process many modalities at once, such as connecting language and multimedia, music and art, sensing and actuation, and more. Through lectures, readings, discussions, and a significant research component, this course will develop critical thinking skills and intuitions when applying AI to new data modalities, knowledge of recent technical achievements in AI, and a deeper understanding of the AI research process.
Dept MASMAS.S60+spring_2025
Education & TeachingOpen learning
How to Speak
Patrick Winston’s How to Speak talk has been an MIT tradition for over 40 years. Offered every January during the Independent Activities Period (IAP), usually to overflow crowds, the talk is intended to improve your speaking ability in critical situations by teaching you a few heuristic rules. Professor Winston’s collection of rules is presented along with examples of their application in job-interview talks, thesis defenses, oral examinations, and lectures. About Professor Winston A professor at MIT for almost 50 years, Patrick Winston was director of MIT’s Artificial Intelligence Laboratory from 1972 to 1997 before it merged with the Laboratory for Computer Science to become MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). He led CSAIL’s Genesis Research Group, which focused on developing a computational account of human intelligence and how human intelligence differs from that of other species, with special attention to modeling human story comprehension. Professor Winston passed away on July 19, 2019.
Dept MITRES.TLL-005+january-iap_2018
MathematicsOpen learning
Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic
This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses. See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response
Dept 6, 1818.S190+spring_2020
LinguisticsOpen learning
Language Processing
This course is a seminar in real-time language comprehension. It considers models of sentence and discourse comprehension from the linguistic, psychology, and artificial intelligence literature, including symbolic and connectionist models. Topics include ambiguity resolution and linguistic complexity; the use of lexical, syntactic, semantic, pragmatic, contextual and prosodic information in language comprehension; the relationship between the computational resources available in working memory and the language processing mechanism; and the psychological reality of linguistic representations.
Dept 9, 249.591J+fall_2004
Machine LearningOpen learning
Machine Learning for Inverse Graphics
This course covers fundamental and advanced techniques in this field at the intersection of computer vision, computer graphics, and geometric deep learning. It will lay the foundations of how cameras see the world, how we can represent 3D scenes for artificial intelligence, how we can learn to reconstruct these representations from only a single image, how we can guarantee certain kinds of generalizations, and how we can train these models in a self-supervised way.
Dept 66.S980+fall_2022
EngineeringOpen learning
Medical Artificial Intelligence
This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.
Dept HSTHST.947+spring_2005
Digital Business & ITOpen learning
Medical Decision Support
This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications. It emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Its technical focus is on decision support, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks, rough sets), and techniques to evaluate the performance of such systems. It reviews computer-based diagnosis, planning and monitoring of therapeutic interventions. It also discusses implemented medical applications and the software tools used in their construction. Students produce a final project using the machine learning methods learned in the course, based on actual clinical data. Lecturers Prof. Stephan Dreiseitl Prof. Ju Jan Kim Prof. Bill Long Prof. Marco Ramoni Prof. Fred Resnic Prof. David Wypij
Dept 6, HSTHST.951J+spring_2003
Digital Business & ITOpen learning
Medical Decision Support
This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.
Dept 6, HSTHST.951J+fall_2005
MathematicsOpen learning
Prediction: Machine Learning and Statistics
Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.
Dept 1515.097+spring_2012
Machine LearningOpen learning
Principles of Autonomy and Decision Making
This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.
Dept 1616.410+fall_2010
MathematicsOpen learning
Probability and Causality in Human Cognition
Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning. This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.
Dept 99.916-A+spring_2003
MathematicsOpen learning
Special Seminar in Applied Probability and Stochastic Processes
This seminar is intended for doctoral students and discusses topics in applied probability. This semester includes a variety of fields, namely statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), computer science (random K-SAT problem, coloring, average case complexity) and electrical engineering (low density parity check (LDPC) codes).
Dept 1515.098+spring_2006
Cognitive ScienceOpen learning
Techniques in Artificial Intelligence (SMA 5504)
6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).
Dept 66.825+fall_2002
Art, Design & ArchitectureOpen learning
The Battlecode Programming Competition
This course is conducted as an artificial intelligence programming contest in Java. Students work in teams to program virtual robots to play Battlecode, a real-time strategy game. Optional lectures are provided on topics and programming practices relevant to the game, and students learn and improve their programming skills experientially. The competition culminates in a live Battlecode tournament. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.
Dept 66.370+january-iap_2013
PhilosophyOpen learning
The Society of Mind
This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.
Dept 66.868J+fall_2011
