The College of Information and Computer Sciences at the University of Massachusetts is pleased to offer its first course at the university's new Boston location at One Beacon St., COMPSCI 589: Machine Learning.
This course is ideal for:
- UMass and non-UMass graduate students and advanced undergraduate students who would like to complete coursework towards their degrees
- Computer science and information technology professionals who want to build their knowledge and expertise in the growing field of machine learning
TIME, LOCATION & COST
The course will be held on Mondays from 6 to 9 pm from January 25, 2016 through April 25, 2016 at One Beacon Street, Lower Level, Boston, MA 02108.
The cost for the 3-credit course is $2,250. Please note that employers will often cover the cost of continuing education courses.
ENROLL TODAY! Need help enrolling? Contact Leeanne Leclerc (413-545-3640 | email@example.com).
This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results. The course will also explore the use of machine learning methods across different computing contexts including desktop, cluster, and cloud computing. The course will include programming assignments, a midterm exam, and a final project. Python is the required programming language for the course. Students are expected to have an undergraduate background in computer science. The course will be closely aligned in structure and content with the COMPSCI 589 course offered by Prof. Ben Marlin on the UMass Amherst campus. 3 credits.
Dr. Rukmini VijaykumarDr. Rukmini Vijaykumar is an independent consultant in the Greater Boston area. Current interest areas include data modeling and analysis, statistical inference, machine learning, and performance optimi zation. She served as Senior Member of Technical Staff at Verizon Communications in Waltham, MA developing automated systems for network fault diagnosis. Prior to that she was a Principal Engineer at MAK Technology working on AI-based research projects. She was an Assistant professor of Computer Science at the University of Southern Maine (1988-1992) where she taught undergraduate and graduate courses in Computer Science and served as the primary resource for AI-based projects and courses. She earned her Ph.D. in Computer Science from UMass Amherst (1989) in Robotics and AI, M.Tech. Computer Science and M.Sc. Mathematics from the Indian Institute of Technology, Madras, and B.Sc. in Mathematics from University of Madras.