• University of Beira Interior

    MACHINE LEARNING


2025/26, Fall

Informatics Engineering (M.Sc.), Artificial Intelligence and Data Science (B.Sc.)


NEWS

01/09/2025: The webpage for the course is online.



PROGRAM

1. Introduction;

2. Model Representation, Linear Regression;

3. Logistic Regresion;

4. Dimensionality Reduction;

5. Neural Networks;

6. Unsupervised and Self-Supervised Learning;

7. Density Estimation;

8. Reinforcement Learning;


BIBLIOGRAPHY

- C. Bishop. Pattern Recognition and Machine Learning, Springer, ISBN-13: 978-0387310732, 2011.

- M. Mohri, A. Rostamizadeh, A. Talwalkar, F. Bach. Foundations of Machine Learning, ISBN-13: 978-0262039406, 2018.


EVALUATION CRITERIA

- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and 80% of the practical classes.

- Practical Projects (P) The practical projects of this course weight 50% (10/20) of the final mark.

- (P1) Practical Project 1: Supervised Learning (Linear + Logistic Regression) (5/20).

- Due Date: Friday, October 3rd, 2025, 23:59:59.

- (P2) Practical Project 2: Convolutional Neural Networks - CNNs (10/20).

- Due Date: Friday, November 14th, 2025, 23:59:59.

- (P3) Practical Project 3: Unsupervised Learning (5/20).

- Due Date: Friday, December 12th, 2025, 23:59:59.

- To get approved at the course, a minimal mark of 8/20 should be obtained in the practical project part.

- Written Test (F) Thursday, December 18th, 2025, 18:00, Room 6.03.

- Mark (M) M = [A >= 0.8] * [P >= 8/20] * (P * 10/20 + F * 10/20).

- Admission to Exams Students with M >= 6 are admitted to final exams.

- The practical projects mark is considered in all examination epochs.




CLASSES

Theoretical slides (Introduction, Taxonomy ML): [pdf]

Practical Sheet I (Google Colab + Python essentials): [pdf]

Practical Project I (Linear + Logistic Regression): [pdf]

Practical Project I (Medical Dataset): [zip]

Practical Project I (MNIST Dataset): [zip]

Theoretical slides (Supervised Classification): [pdf]

Practical Sheet II (Linear Regression): [pdf]

Pizza Dataset: [csv]

Linear Regression (solution) [py]

(P1 Submission Deadline: October, 3rd, 23:59:59, hugomcp@ubi.pt)

(P2 Submission Deadline: November, 14th, 23:59:59, hugomcp@ubi.pt)

(P3 Submission Deadline: December, 12th, 23:59:59, hugomcp@ubi.pt)

(Written Test, December, 18th, 18:00, Room 6.03.)





EVALUATION



FACULTY

HUGO PEDRO PROENÇA


Informatics Department

Theoretical + Practical classes