Discover the vital role unsupervised learning techniques play in AI by allowing models to learn from unlabeled data.
6 weeks,
excluding orientation
6–8 hours per week,
self-paced learning online
Weekly modules,
flexible learning
1
Machine learning (ML) techniques to harness the power of unused data within your business, and the ability to create transparent, interpretable ML models.
2
An in-depth understanding of how representation learning can address business problems and increase ROI on AI initiatives.
3
Insight into the challenges, opportunities, and important considerations of generative models in an organization.
4
A holistic view of the landscape of pre-trained models and how to best utilize these models in your organization.
Unsupervised learning algorithms are particularly valuable in generative AI as they can analyze large datasets without the need for explicit labels or predefined categories. Instead, they focus on discovering inherent patterns, relationships, and representations within the data itself.
Discover how AI learns, the impact of representations, the fundamentals of generative models, and how to leverage pre-trained models in your context in this online short course.
Over the duration of this online short course, you’ll work through the following modules:
MODULE 1 THE POTENTIAL OF DATA
Explore how machine learning (ML) techniques are defining the potential of data.
MODULE 2 LEARNING AND LEVERAGING REPRESENTATIONS
Discover how representations can dramatically reduce the quantity of labels needed to build accurate AI models.
MODULE 3 GENERATIVE MODELS
Discover what generative models are and the significance of transforming between different data formats.
MODULE 4 AI BUILDING BLOCKS
Learn how pre-trained AI models can impact the deployment of representation learning and generative modeling in organizations.
MODULE 5 ADAPTING AI TOOLS
Discover the importance of interpretability and causality in building accurate ML models.
MODULE 6 CHALLENGES AND THE FUTURE
Explore the realities of deploying ML models in your organization.
Please note that module titles and their contents are subject to change during course development.
Antonio Torralba
Delta Electronics Professor of Electrical Engineering and Computer Science, Head of AI+D Faculty, EECS Department, MIT CSAIL
Torralba’s research is in the areas of computer vision, machine learning, and human visual perception. He is interested in building systems that can perceive the world like humans do, and modalities such as audition and touch. Other interests include understanding neural networks, common-sense reasoning, computational photography, building image databases, and the intersections between visual art and computation.
Vivek Farias
Patrick J. McGovern (1959) Professor, Professor of Operations Management, MIT Sloan
Farias’s research focuses on the development of new methodologies for large-scale dynamic optimization under uncertainty, and the application of these methodologies to the design of practical revenue management strategies across various industries ranging from airlines and retail to online advertising.
Phillip Isola
Associate Professor in EECS, MIT
Isola and his current research group study how to make AI more like natural intelligence. He is currently focused on representation learning, generative modeling, and multiagent systems. He has been a visiting research scientist at OpenAI and a postdoctoral scholar within the EECS department at UC Berkeley. He completed his PhD in brain and cognitive sciences at MIT and received his undergraduate degree in computer science from Yale.
Jacob Andreas
Assistant Professor, MIT CSAIL
Andreas is the head of MIT's Language and Intelligence Group, which is working toward a future in which everyone can interact with software using the languages they already speak. He is a member of CSAIL and the Department of Electrical Engineering and Computer Science, and is the X Consortium assistant professor at MIT in the EECS and in CSAIL.
Rama Ramakrishnan
Professor of the Practice, Data Science, and Applied Machine Learning, MIT Sloan
Ramakrishnan’s research and teaching interests center on the application of data science and machine learning techniques to problems in the industry and in the creation of products and services made intelligent by the algorithmic use of data.
This MIT Sloan and MIT Schwarzman College online short course is delivered in collaboration with online education provider GetSmarter. Join a growing community of global professionals, and benefit from the opportunity to:
Develop new competencies and earn valuable recognition from an international selection of universities and institutions, entirely online and on your own time frame.
Enjoy a personalized, people-mediated online learning experience created to make you feel supported at every step.
Experience a flexible but structured approach to online education as you plan your learning around your life to meet weekly milestones.