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MIT Professional Education – Short Programs Provides Opportunities to Learn Directly from CSAIL Faculty
Advanced Study Program
The Advanced Study Program provides professionals in industry and government the opportunity to enroll in MIT credit courses to further their knowledge for their organization and to advance their own careers. If you are interested in taking ChemE related courses for one or more semesters, on a full or part-time basis, while still working and contributing to your company, the Advanced Study Program provides you the vehicle to do just that! Follow the Course Listing link to see a list of all of the courses available. Earn grades, MIT credit, and a certificate of completion.
For more information about the MIT Professional Education Advanced Study Program, please visit http://web.mit.edu/professional/advanced-study/index.html
This summer, MIT Professional Education – Short Programs is offering 1-5 day short courses taught by MIT faculty and experts, including members of the Computer Science and Artificial Intelligence Laboratory (CSAIL). These courses provide an opportunity to learn crucial knowledge and skills from some of the top experts in their respective fields, in areas of MIT expertise. For complete details on all of the available courses, visit the Short Programs website.
Below is a list of courses taught by members of the MIT Computer Science and Artificial Intelligence Laboratory:
Leadership Skills for Engineering and Science Faculty
C. Leiserson, C. McVinney
This course focuses on human-centered strategies for leading effective teams in technical academic environments. Through a series of interactive role-playing activities, self-assessment instruments, and group discussions, you will develop a repertoire of techniques for addressing issues that commonly arise within engineering research groups and teaching staff.
July 13-14, 2015 | $1,600 | 1.4 CEUs
Machine Learning for Big Data and Text Processing
T. Jaakkola, R. Barzilay
Machine learning methods drive much of modern data analysis across engineering, sciences, 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. Much of today's data is available in unstructured and semi-structured form (system/user trails, descriptions, transactions, or social media data), requiring effective tools for turning such data into useful predictions or insights. This course examines a suite of machine learning tools and their applications, including predictive analysis. We will discuss insights underlying these 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.
June 8-12, 2015 | $3,750 | 2.8 CEUs
For more information about the MIT Professional Education Short Programs, please visit http://web.mit.edu/professional/short-programs/index.html.