The Machine Learning Specialization is oriented to the design, development and testing of new techniques and processes for professional application, which responds to demands from social and productive sectors, public and private.
The activities to be developed include scientific training disciplines along with other units that broaden the conceptual framework of the problems raised.
It is based on the Machine Learning and Computational Statistics courses that are part of the MicroMaster in Statistics and Data Science offered by MITx through the edX platform with weekly support from facilitators from MIT and UTEC. In turn, an intensive 4-week workshop is held with MIT in Uruguay (date subject to sanitary conditions). In the Specialization, a final monographic work is required, on a topic of your choice, focused on the analysis and review of existing bibliography, in addition to the presentation of your personal point of view on the subject, linked to the methods and tools learned.
More info about the specialization here.
Registrations can be made until June 25, 2021. The semi-face-to-face diploma begins on July 5, 2021 with an estimated average dedication of 20 hours per week and a price of USD 4,200 (Early bird 10% off until June 14) . The curricular activities associated with the Diploma comprise a total of 58 credits, which according to the General Regulations for Studies of the UTEC represents 870 chronological hours, which implies that the student must dedicate an average of 20 hours per week to its completion.
The demand for specialists with machine learning knowledge is on the rise both locally and regionally. The Specialization in Machine Learning aims to train its participants in the development of computational solutions that use machine learning. The graduate will develop a technical vision that will allow him to identify problems or areas of opportunity related to data analysis through the use of technological tools. You will be able to apply descriptive, diagnostic and predictive analytical methodologies to optimize productive, administrative, financial and / or technological processes, in addition to promoting decision-making based on specific facts and data. From a technical point of view, graduates will be able to:
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