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Celebrate the Day of AI with 5 Online AI and Machine Learning Resources from MIT

AI offers new ways for learners of all ages to tackle real-world challenges

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MIT’s recent first annual Day of AI offered thousands of K-12 educators around the world a free series of hands-on activities intended to introduce students to artificial intelligence (AI) and help them explore how AI plays a part in their lives today. Such technologies offer exciting new ways for learners of all ages to tackle real-world challenges. The Day of AI exposed learners of all age to the key concepts, applications, and implications of these new computational methods.

MIT offers a wide variety of online educational resources for learning about AI and machine learning. Here are five online artificial intelligence resources from MIT, for learners of all levels.

  1. Artificial Intelligence from MIT OpenCourseWare

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.

This is a free resource from MIT OpenCourseWare.

2. Media Literacy in the Age of Deepfakes from MIT OpenCourseWare and the MIT Center for Advanced Virtuality

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. The materials in this course will help you contextualize some key terms related to misinformation, focus on the proliferation of deepfakes within our media environment, and explore synthetic media for the civic good, including AI-enabled projects geared towards satire, investigative documentary, and public history.

This is a free resource from MIT OpenCourseWare.

3. Introduction to Machine Learning from MIT OpenCourseware

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 is a free resource from MIT OpenCourseWare.

4. Machine Learning with Python from MITx

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course can be taken on its own, or as part of the MITx MicroMasters® Program in Statistics and Data Science

5. MicroMasters Program in Statistics and Data Science from MITx

Comprised of four online courses and a virtually proctored exam, this program will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. Learners will dive into the fundamentals of probability and statistics as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. The program certificate can be applied, for admitted students, towards a PhD in Social and Engineering Systems (SES) through the MIT Institute for Data, Systems, and Society (IDSS) or may accelerate your path towards a Master’s degree at other universities around the world.

As AI and machine learning continue to gain prominence in the global workforce, engineers and other STEM professionals who lack data science knowledge or experience in modern computational methods might feel left behind. For these professionals and their employers, bridging the gap with online learning is essential, whether it be through free AI resources or online certificate courses in AI and machine learning.

Not sure where to start? Browse a wide array of online courses and resources available through MIT Open Learning.

Originally published at http://curve.mit.edu.

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