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.

Preliminary Content

Python Basics:

• General overview: link (Part II - L03 and L04);

  • Similar material in portuguese: link.

Mathematical Background:

• Linear Algebra: slides, video;

• Probability and Statistics: slides, video;

• Optimization: video, slides.

Track on Signals, Images, and Patterns: link.

Week 2 - 22/08

Lecture 2 - Training Simple Machine Learning Algorithms

• Slides (link);

• Videos (link1, link2);

• Codes (link).

Week 4 - 05/09

Lecture 4 - Building Good Training Sets - Data Preprocessing

• Slides (link);

• Videos (link);

• Codes (link).

Week 6 - 19/09

Assignment 1 - Python and Machine Learning Basics

• Guide (link).

Week 8 - 03/10

Assignment 2 - Classification with scikit-learn

• Guide (link).

Week 10 - 24/10

• Assignment 3 - Real Problem - cont.

  • Guide (link).

Week 12 - 07/11

Assignment 4 - Embedded Machine Learning

• Guide (link).

Week 17 - 19/12

Presentation of the final project.