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Fundamentals of Machine Learning 


April 2021



 About this course 


This course serves as an introduction to the concept of machine learning, which is commonly used in artificial intelligence systems. We will learn how to make automatic classification and prediction projects, based on previously observed data, using classical prediction methods.

-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-Python programming-Linear algebra -Laptop with at least 8 GB of RAM
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.


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

-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. We will be using Python 3.7.X. with the modules NumPy, Pandas, Pickle, Matplotlib, Imageio, Scikit-Learn and Seaborn.


Class Schedule & Syllabus



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. 


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