Pattern Recognition and Machine Learning
2022/02
General Information
Objective: Study of the main machine learning methods and their applications.
Syllabus: Machine learning basics; Python and Scikit learn; Data preprocessing; Basic Classifiers; Dimensionality Reduction; Hyperparameter tuning; Multilayer Artificial Neural Network; TinyML.
Duration: 60 hours (17 weeks).
Time: Mondays (17:50 - 21:10) - at CB301.
Grade: Assignments (50%) and Final Project (50%).
TinyML: Combined use of Arduino Nano 33 BLE Sense (link) with Edge Impulse (link).
Lecturer: André Eugenio Lazzaretti.
Bibliography and Support Materials
Book:
Raschka, S. Python Machine Learning. Packt, 2020
Other Courses:
Machine Learning course using the same book (by Prof. Sebastian Raschka): link.
Similar material in portuguese: link.
Andrew Ng at Coursera.
Track on Signals, Images, and Patterns: link.
Week 10 - 24/10
Assignment 3 - Real Problem - cont.
Guide (link).
Week 17 - 19/12
Presentation of the final project.