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; Neural Networks, Clustering; Recent Topics.

Duration/credits: 12 weeks - three credits.

Time: Fridays (08:20 - 12:00) - online synchronous classes.

Grade: Assignments (20%) and Final Project (80%).

Lecturer: André Eugenio Lazzaretti.

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.

Background:

• Linear Algebra:

  • • Slides (link);

    • Video (link1, link2).

• Vector Calculus:

  • • Slides (link);

    • Video (link).

• Probability and Statistics:

  • • Slides (link);

    • Video (link).

• Optimization:

  • • Slides (link);

    • Video (link).

• Python Basics: link (Part II - L03 and L04).