Machine learning methods drive much of modern data analysis across engineering, science, and commercial applications. For example, search engines, recommender systems, advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.
This course looks at how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied in different fields, even when using unstructured data. You'll gain insights about the underlying tools, what kinds of problems they can/cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications, particularly in the healthcare field.
EARN A PROFESSIONAL CERTIFICATE IN MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Advanced Machine Learning for Big Data and Text Processing may be taken individually or as a core course for the Professional Certificate Program in Machine Learning and Artificial Intelligence.
- Registration opening soon
- Understand broad opportunities for automation with machine learning
- Outline key aspects of practical problems that are likely to impact performance
- Explore modern natural language processing tools, formulations, and problems
- Be able to discuss scaling issues (amount of data, dimensionality, storage, and computation)
- See through the process of applying machine learning methods in practice, foresee likely hurdles and possible remedies
- Grasp what predictive analytics often does not provide
- Understand current machine learning trends and opportunities that they bring
Who Should Attend:
This course is designed for people with working knowledge and experience with machine learning. Those who attend should have a basic understanding of the essential mathematical concepts and theories used in the field. The course assumes an undergraduate degree in computer science or another technical area such as statistics, physics, electrical engineering, etc., with exposure to vectors and matrices, basic concepts of probability. A high-level understanding of programming (thinking in terms of programs) is also beneficial.
- For professionals whose work involves data hands-on, the course aims to provide a deeper understanding and sharper intuitions about what is possible, what is not, and which methods to consider in what contexts.
- At the managerial level, the course provides the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data.
Laptops are required for this course. Tablets will not be sufficient for the computing activities performed in this course.
Day 1: (6h)
- Recommender systems (2h)
- Unsupervised learning: mixtures, EM (2h)
- Markov models, recurrent neural networks (2h)
Day 2: (6h)
- Reinforcement learning (2h)
- Deep RL (1h)
- Intro to NLP problems (1h)
- Learning lexical representations (1h)
- Extraction, annotation, parsing (1h)
Day 3: (6h)
- Advanced NLP applications: machine translation, dialogue systems (2h)
- ML for medical applications and drug design (2h)
- Participant problems, solicited in advance (2h)
This course runs 9:00 am - 5:00 pm Wednesday through Friday.
Regina Barzilay is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing. She is a recipient of various awards including of the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
Tommi Jaakkola is a professor of Electrical Engineering and Computer Science and also a member of the Computer Science and Artificial Intelligence Laboratory. He received M.Sc. in theoretical physics, and Ph.D. from MIT in computational neuroscience. His work pertains to inferential, algorithmic and estimation questions in machine learning, including large scale probabilistic distributed inference, deep learning, and causal inference. The applied side of his work has focused on problems in natural language processing such as parsing, regulatory models in computational biology, computational chemistry, and recommender systems. He received the Sloan Research Fellowship 2002 and many awards for his publications, across areas.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.