General Information
Objective: Study of the main machine learning methods and applications.
Syllabus: Machine learning basics; Python and Scikit-learn; Data preprocessing; Classifiers; Dimensionality Reduction; Hyperparameter tuning; Regression; Clustering; Recent Topics.
Duration/credits: 12 weeks - three credits.
Time: Fridays (13:50 - 17:30) - online synchronous (SYN) and asynchronous (ASYN) classes (videos).
Grade: Partial Exams (30% each) and Final Exam (40%).
Lecturer: André Eugenio Lazzaretti.
Bibliography and Support Materials
Books:
Theory:
Bishop, C. Pattern Recognition and Machine Learning. Springer, 2006.
Theodoridis, S. Machine Learning: A Bayesian and Optimization Perspective. Academic Press, 2020.
Deisenroth, M. P.; Faisal, A. A. & Ong, C. S. (2020), Mathematics for Machine Learning, Cambridge University Press.
Practical:
Raschka, S. Python Machine Learning. Packt, 2020.
Week 1 - 16/06
Week 3 - 30/06
(SYN) Lecture 5 - Introduction to Machine Learning
(SYN) Lecture 6 - Training Simple Machine Learning Algorithms
(SYN) Lecture 7a - A Tour of Machine Learning Classifiers Using scikit-learn - part 1 (linear)
(ASYN) Assignment 3
Guide (link).
Week 5 - 14/07 - ASYN
(ASYN) Assignment 4 - Python and Machine Learning Basics
Guide (link).
Week 6 - 21/07 - ASYN
(ASYN) Assignment 5 - Real Problem
Guide (link).
Week 9 - 11/08