Pattern Recognition and Machine Learning
2022/01
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; Regression; Clustering.
Duration/credits: 12 weeks - three credits.
Time: Mondays (17:50 - 21:10) - synchronous (SYN) at CB301 (B block - map) and asynchronous (ASYN) classes (videos).
Grade: Assignments (50%) and Final Project (50%).
Advanced materials: highly suitable for *doctoral* or masters students who will have machine learning as the core of their work.
Lecturer: André Eugenio Lazzaretti.
Bibliography and Support Materials
Book:
Raschka, S. Python Machine Learning. Packt, 2020
Other Courses:
Week 3 - 21/03
(ASYN) Lecture 3 - A Tour of Machine Learning Classifiers Using scikit-learn
Slides (link);
Codes (link);
Complementary material for Scikit-learn: link (Part II - L05);
Similar material in portuguese: link.
Details on Multi-class Classification (video).
Week 8 - 25/04
(ASYN) Lecture 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
More detailed information (advanced material): video.
(ASYN) Lecture 7 - Predicting Continuous Target Variables with Regression Analysis
Week 11 - 16/05
(ASYN) Lecture 9 - Working with Unlabeled Data – Clustering Analysis