Machine Learning 2026-II
Isaac CAICEDO-CASTRO, Ph.D.
Ph.D. in Computer Science, University of Grenoble Alpes (France)
Ph.D. in Engineering, National University of Colombia
Full Professor of Computer Science at the University of Córdoba (Colombia)
Topics:
Introduction
Classification and Regression
Machine Learning: a probabilistic approach
Artificial Neural Networks
Clustering
Announcements:
Welcome to EP411113 (Machine Learning), 2026-II
Click here to join the monthly virtual lecture through Google Meet (synchronous class mode).
You can download the Dataset for regression by clicking here
Office hours
Fridays, 3:00 PM - 5:00 PM, Telecommunications and Systems Department bureau. You must request an appointment in advance by email; in the message, you ought to describe your doubt in detail. If you don't live in Montería, I'll reply with a Google Meet link to join a meeting. In the subject, you must indicate the course name, your group, and the subject itself; otherwise, I might ignore the message.
Evaluation
The course is split into three sessions; in each one, the coursework weighs the same.
The coursework might be assignments, exams, or a project
In the project, you might work alone or team up with another classmate, however, both members of the team must present evidence of their contributions.
Late coursework won't be accepted
Resources:
Machine Learning: A Probabilistic Approach by Kevin Murphy (main bibliographical reference)
Information Theory, Inference, and Learning Algorithms by MacKay
Kaggle website. If you want to learn to harness machine learning, you ought to become a kaggler.
UC Irvine Machine Learning Repository. If you need a dataset to work with, this is the right place to start your searching
Handwritten training set at Hastie's webpage. Normalized handwritten digits, automatically scanned from envelopes by the U.S. Postal Service.