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.

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

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.

How are math, art, music, and language intertwined? How does intelligent behavior arise from its component parts? Can computers think? Can brains compute? Douglas Hofstadter probes very cleverly at these questions and more in his Pulitzer Prize winning book, "Gödel, Escher, Bach". In this seminar, we will read and discuss the book in depth, taking the time to solve its puzzles, appreciate the Bach pieces that inspired its dialogues, and discover its hidden tricks along the way.

The projects in this guide use a student-driven approach to learning. Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from machine learning models to video games—to demonstrate their learning.

Project 1: Programming with Machine Learning
Project 2: AI-Powered Players in Video Games
Project 3: Using AI for Robotic Motion Planning
Project 4: Machine Learning as a Service

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society. Several key approaches were taken into consideration in the design of these projects. Understanding these approaches will support both your understanding and implementation of the projects in this guide, as well as your own work to design further activities that integrate AI education into your curriculum.

Project 1: AI Chatbots
Project 2: Developing a Critical Eye
Project 3: Using AI to Solve Environmental Problems
Project 4: Laws for AI

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

In this guide, students’ exploration of AI is framed within the context of ethical considerations and aligned with standards and concepts, and depths of understanding that would be appropriate across various subject areas and grade levels in K–12. Depending on the level of your students and the amount of time you have available, you might complete an entire project, pick and choose from the listed activities, or you might take students’ learning further by taking advantage of the additional extensions and resources provided for you. For students with no previous experience with AI education, exposure to the guided learning activities alone will create an understanding of their world that they likely did not previously have. And for those with some background in computer science or AI, the complete projects and resources will still challenge their thinking and expose them to new AI technologies and applications across various fields of study.

Project 1: Fair's Fair
Project 2: Who is in Control?
Project 3: The Trade-offs of AI Technology
Project 4: AI and the 21st Century Worker

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education.

As the digital revolution brings with it radical changes in how and what we learn, people must continue to learn all the time. New technologies make possible new approaches to learning, new contexts for learning, new tools to support learning, and new ideas of what can be learned. This course will explore these new opportunities for learning with a special focus on what can be learned through immersive, hands-on activities. Students will participate in (and reflect on) a variety of learning situations, and will use Media Lab technologies to develop new workshops, iteratively run and refine the workshops, and analyze how and what the workshop participants learn.

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.

The strategic importance of information technology is now widely accepted. It has also become increasingly clear that the identification of strategic applications alone does not result in success for an organization. A careful coordination of strategic applications, information technologies, and organizational structures must be made to attain success. This course addresses strategic, technological, and organizational connectivity issues to support effective and meaningful integration of information and systems. This course is especially relevant to those who wish to effectively exploit information technology and create new business processes and opportunities.

This is an introductory course on computational thinking. We use the Julia programming language to approach real-world problems in varied areas, applying data analysis and computational and mathematical modeling. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Topics include image analysis, particle dynamics and ray tracing, epidemic propagation, and climate modeling.

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

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.

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.

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.

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.

Media Literacy in the Age of Deepfakes aims to equip students with the critical skills to better understand the past and contemporary threat of misinformation. Students will learn about different ways to analyze emerging forms of misinformation such as "deepfake" videos as well as how new technologies can be used to create a more just and equitable society. This module consists of three interconnected sections. We begin by defining and contextualizing some key terms related to misinformation. We then focus on the proliferation of deepfakes within our media environment. Lastly, we explore synthetic media for the civic good, including AI-enabled projects geared towards satire, investigative documentary, and public history. In Event of Moon Disaster, an award-winning deepfake art installation about the "failed" Apollo 11 moon landing, serves as a central case study.
This learning module also includes a suite of educator resources that consists of a syllabus, bibliography, and design prompts. We encourage teachers to draw on and adapt these resources for the purposes of their own classes.
Visit Media Literacy in the Age of Deepfakes to access the learning module and educator resources. A sample of some of these materials can be found on OCW.
This course was produced by the MIT Center for Advanced Virtuality, with support from the J-WEL: Abdul Latif Jameel World Education Lab.

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.

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.

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