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 is an introduction to many of the central issues in a branch of philosophy called philosophy of mind.

This customized independent study course puts Sloan Fellows MBA students into direct contact with innovators tackling global needs in education, healthcare, and energy/environment. Co-designed projects address low-income markets in the U.S. or globally, focusing on the application of new ideas and technology rooted in MIT innovations or the Boston ecosystem. Every project aims to develop better ways for the right innovations to reach scale, sustainability, and quality, thereby improving lives and uncovering opportunities in underserved markets.

Los proyectos de esta guía utilizan un enfoque centrado en los alumnos para el aprendizaje. En lugar de solo aprender acerca de la IA con videos o conferencias, los alumnos que realizan estos proyectos son participantes activos en la exploración de ella. En el proceso, los estudiantes trabajarán directamente con tecnologías innovadoras de IA, participarán en actividades no en línea para ampliar su comprensión de cómo funcionan las tecnologías de IA y crearán diversos productos auténticos
desde modelos de aprendizaje automático hasta videojuegos— para demostrar su aprendizaje.

PROYECTO 1: Programación con aprendizaje automático
PROYECTO 2: Jugadores asistidos por IA en videojuegos
PROYECTO 3: Uso de la IA para planificar movimientos robóticos
PROYECTO 4: El aprendizaje automático como un servicio

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Lo que la IA hace bien y lo que no hace tan bien
PROYECTO 2: Datos de entrenamiento y aprendizaje automático
PROYECTO 3: Los sentidos comparados con los sensores
PROYECTO 4: Navegación e IA

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Chatbots de IA
PROYECTO 2: Desarrollo de una mirada crítica
PROYECTO 3: Uso de la IA para resolver problemas del medio ambiente
PROYECTO 4: Leyes para la IA

En esta guía, la exploración de la IA por parte de los alumnos se enmarca en el contexto de las consideraciones éticas, y en concordancia con los estándares, conceptos y profundidad adecuados para varias materias de K–12. Dependiendo del nivel de sus alumnos y la cantidad de tiempo que tenga disponible, puede completar todas las actividades de Inicio hasta las actividades de Demostraciones culminantes; puede seleccionar actividades de la lista; o puede llevar el aprendizaje de los alumnos más lejos, aprovechando las extensiones y recursos adicionales proporcionados. Para los alumnos sin experiencia previa de formación en la IA, la exposición misma a las actividades de aprendizaje guiadas creará una comprensión de su mundo que probablemente no tenían antes. Y para aquellos con conocimientos previos en informática o con la IA, los proyectos y recursos completos seguirán desafiando su razonamiento y los expondrán a nuevas tecnologías y aplicaciones de la IA en diversos campos de estudio.

PROYECTO 1: Lo justo es justo
PROYECTO 2: ¿Quién tiene el control?
PROYECTO 3: Las ventajas y desventajas de la tecnología de la IA
PROYECTO 4: La IA y el trabajador del siglo XXI

RAISE (Responsible AI for Social Empowerment and Education) is a new MIT-wide initiative headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. MIT researchers continually develop curriculum modules and associated teaching materials that are available to all K-12 educators for free under a Creative Commons license.

Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based).
Homework assignments will guide students through building a software stack that will enable a robotic arm to autonomously manipulation objects in cluttered scenes (like a kitchen). A final project will allow students to dig deeper into a specific aspect of their choosing. The class has hardware available for ambitious final projects, but will also make heavy use of simulation using cloud resources.

Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing, works to train students and facilitate research to assess the broad challenges and opportunities associated with computing, and improve design, policy, implementation, and impacts.
This site is a resource for SERC pedagogical materials developed for use in MIT courses. SERC brings together cross-disciplinary teams of faculty, researchers, and students to develop original pedagogical materials that meet our goal of training students to practice responsible technology development through incorporation of insights and methods from the humanities and social sciences, including an emphasis on social responsibility.
Materials include the MIT Case Studies Series in Social and Ethical Responsibilities of Computing, original Active Learning Projects, and lecture materials that provide students hands-on practice and training in SERC, together with other resources and tools found useful in education at MIT. Original homework assignments and in-class demonstrations are specially created by multidisciplinary teams, to enable instructors to embed SERC-related material into a wide variety of existing courses.
The aim of SERC is to facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies, and fostering the creation of technologies in the public interest.

Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines.
This course introduces nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.

It describes the basic character of AI in the term of STA (Sense, Think, Act)

 The organizations that depend largely on collecting data from various sources or are highly digitized must adopt data security. It is better to fight the risks at the initial stage than to regret the loss of data and face the consequences. If the information can not be kept safe from various attacks then the preference of the organization will decrease eventually. Even if personal information cannot be trusted in the hands of the organization then there will be dissatisfaction among customers. If an organization is unable to keep its customers satisfied then its value can hit rock bottom. Hence, by using Artificial Intelligence and Machine Learning the data security should be made better. These technologies will also help in decreasing the extra effort that has to be put by an organization and its employees.