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Unsupervised Machine Learning 

Dates

July 2021

 

 

 About this course 

 

This course explores techniques for machine learning from unlabeled data. You will learn how to find patterns and associations from the data, and use this knowledge to detect anomalies, create recommendation engines, and to improve supervised classification systems. We will then explore how to generate realistic fake data using generative adversarial networks.

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-Courses: Fundamentals of Machine Learning and Deep Learning-Participants must be proficient in English and have achieved a grade of at least A2 on the free online test https://www.examenglish.com/leveltest/index.php-Laptop with at least 8 GB of RAM
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-The course consists of four online meetings, 3 hours each. During the sessions, the instructor will make presentations and demonstrations, and students will be asked to solve small practices during the class.

 Evaluation 


-Attendance is mandatory and any student who does not attend at least 90% of the meetings will fail the course automatically. All classes will be online.

-The grade will be based on attendance and class participation (40%) and small practices (60%).

-A missed class can be made up if the student conducts a self-study of the material and presents a summary of it to the instructor.

 

 Course Materials 

 

We will use open-source tools only. Python 3.7.X. will be used with the modules TensorFlow and Keras, among others.

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Class Schedule & Syllabus

DOWNLOAD SYLLABUS
JuanCarlos

DR. JUAN CARLOS ROJAS

Professor of Electrical and Computer Science Director, Electrical Exploration Lab

Dr. Juan Carlos Rojas has a master’s and a Ph.D. in electrical engineering from Northeastern University, and a master’s in engineering management from Stanford University. He has led research and development teams for multiple companies in Boston, Silicon Valley, and Costa Rica. Currently, he is an instructor of both electrical engineering and computer science at Texas Tech University-Costa Rica, where he leads the Electrical Exploration Laboratory. 

 

For more information

 
 
Contact us here
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 +(506) 2519-9900| Edificio AE205, Texas Tech University-Costa Rica. Avenida Escazu, San Rafael, Escazú. San José, Costa Rica