Pattern Recognition and Machine Learning

2023/02

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; Clustering; Recent Topics.

Duration/credits: 12 weeks - three credits.

Time: Fridays (13:50 - 17:30) - online synchronous (SYN) and asynchronous (ASYN) classes (videos).

Grade: Partial Exams (30% each) and Final Exam (40%).

Lecturer: André Eugenio Lazzaretti.

Bibliography and Support Materials

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.

Week 1 - 16/06

(SYN) Lecture 0 - Dates, grades, admin
- Slides (link);
- Video (link).
(SYN) Lecture 1 - Linear Algebra
- Slides (link);
- Video (link1, link2).
(SYN) Lecture 2 - Vector Calculus
- Slides (link);
- Video (link).
(ASYN) Python Basics:
- General overview: link (Part II - L03 and L04).
(ASYN) Assignment 1
- Guide (link).

Week 2 - 23/06

(SYN) Lecture 3 - Probability and Statistics
- Slides (link);
- Video (link).
(SYN) Lecture 4 - Optimization
- Slides (link);
- Video (link).
(ASYN) Assignment 2
- Guide (link).

Week 3 - 30/06

(SYN) Lecture 5 - Introduction to Machine Learning

Slides (link);
Videos (link).

(SYN) Lecture 6 - Training Simple Machine Learning Algorithms
- Slides (link);
- Video (link);
- Codes (link).
(SYN) Lecture 7a - A Tour of Machine Learning Classifiers Using scikit-learn - part 1 (linear)
- Slides (link);
- Video (link);
- Codes (link).
- SVM - extra:
  - Slides (link);
  - Video (link).
(ASYN) Assignment 3
- Guide (link).

Week 4 - 07/07

(SYN) Lecture 7b - A Tour of Machine Learning Classifiers Using scikit-learn - part 2 (nonlinear)
- Slides (link);
- Video (link);
- Codes (link).
(SYN) Lecture 7c - Building Good Training Sets - Data Preprocessing
- Slides (link);
- Video (link);
- Codes (link).

Week 5 - 14/07 - ASYN

(ASYN) Assignment 4 - Python and Machine Learning Basics
- Guide (link).

Week 6 - 21/07 - ASYN

(ASYN) Assignment 5 - Real Problem
- Guide (link).

Week 7 - 28/07

(SYN) Lecture 8 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Slides (link);
- Videos (link);
- Codes (link).

(ASYN) Assignment 6 - Real Problem cont.
- Guide (link).

Week 8 - 04/08

First Exam

Grades (link)

Week 9 - 11/08

(SYN) Lecture 9 - Predicting Continuous Target Variables with Regression Analysis
- Slides (link);
- Videos (link);
- Codes (link).
(SYN) Lecture 10 - Compressing Data via Dimensionality Reduction
- Slides (link);
- Videos (link);
- Codes (link).
(ASYN) Assignment 7 - Dimensionality Reduction
- Guide (link).

Week 10 - 18/08

(SYN) Lecture 11 - Working with Unlabeled Data
- Slides (link);
- Videos (link);
- Codes (link).
(SYN) Lecture 12 - Anomaly Detection
- Slides (link);
- Videos (link);
- Codes (link).

(ASYN) Assignment 8 - Clustering
- Guide (link).

Week 11 - 25/08

(SYN) Lecture 13 - Probabilistic Models
- Slides (link);
- Videos (link1, link2).
(SYN) Lecture 14 - Recent Topics
- Slides (link);
- Videos (link).
(ASYN) Assignment 9 - Probabilistic Models
- Guide (link).

Week 12 - 01/09

(SYN) Lecture 15 - Reinforcement Learning
- Slides (link);
- Videos (link);
- Codes (link1, link2).

Week 13 - 15/09

Second Exam

Grades (link)

Week 14 - 06/10

Final Exam (link)

Grades (link)