Cronograma 2021/02

Preliminary content:

Python:
- 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.

Complementary material:

Machine Learning course using the same book (by Prof. Sebastian Raschka): link.
- Similar material in portuguese: link.

Schedule:

16/06 - Lecture 0 (dates, grades, etc):
- Slides (link);
- Video (link).
16/06 - Lecture 1 - Introduction:

21/06 - Lecture 2 - Training Simple Machine Learning Algorithms

23/06, 28/06 - Lecture 3 - A Tour of Machine Learning Classifiers Using scikit-learn

Slides (link);
Videos (link1, link2, link3);
Codes (link);
Complementary material for Scikit-learn: link (Part II - L05);
- Similar material in portuguese: link.
More detailed information (advanced material):
- Logistic Regression: video;
- Linear Support Vector Machines: video;
  - Optimization for SVMs: video1 and video2.
- Nonlinear Support Vector Machines: video1, video2 and video3;
- Decision Trees and Random Forest: video1 and video2.

30/06, 05/07 - Lecture 4 - Building Good Training Sets - Data Preprocessing

07/07, 12/07 - Assignment 1 - Python and Machine Learning Basics

14/07, 19/07 - Lecture 5 - Compressing Data via Dimensionality Reduction

21/07, 26/07 - Assignment 2 - Classification with scikit-learn

28/07, 02/08 - Lecture 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning

04/08, 09/08 - Lecture 7 - Implementing a Multilayer Artificial Neural Network from Scratch

Slides (link);
Videos (link);
Codes (link);
More detailed information (advanced material, including Backpropagation): video.

11/08, 16/08 - Assignment 3 - Real Problem

18/08, 23/08 - Assignment 4 - Embedded Machine Learning - part 1

25/08, 30/08, 01/09 - Assignment 4 - Embedded Machine Learning - part 2

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