Università degli Studi di Pavia

Facoltà di Ingegneria

Deep Learning

A.A. 2020-2021

Second Semester

Fri: 11:00 a.m. - 1:00 p.m., Aula 3

Fri: 2:00 p.m. - 4:00 p.m., Aula D8

Lectures & Suggested Readings:

Reports of errors in the resources below are always welcome

1. 2021.03.12 (theory)

   Introduction [pdf]

2. 2021.03.12 (theory)

   Fundamentals: Artificial Neural Networks [pdf]

3. 2021.03.19 (theory)

   Flow Graphs and Automatic Differentiation [pdf]

4. 2021.03.26 (theory)

   Deep Networks [pdf]

5. 2021.03.26 (lab)

   Google Colab intro [pdf]

   TensorFlow: Variables and Functions [ipynb]

   Executing notebooks in Google Colab: please make sure to create a copy in your drive before using
6. 2021.04.09 (theory)

   Learning as Optimization [pdf]

7. 2021.04.09 (lab)

   Linear Regression [ipynb]

   Apropos Randomization [ipynb]

8. 2021.04.16 (theory)

   Deep Convolutional Neural Networks [pdf]

9. 2021.04.16 (lab)

   Multinomial Logistic Regression (Softmax) [ipynb]

10. 2021.04.23 (lab)

    Convolutional Networks [ipynb]

11. 2021.04.30 (theory)

    Deep Convolutional Neural Networks and Beyond [pdf]

12. 2021.05.07 (theory)

    A Few Relevant Asides [pdf]

13. 2021.05.07 (lab)

    Transfer Learning [ipynb]

14. 2021.05.14 (theory)

    Deep Learning and Time Series [pdf]

15. 2021.05.14 (lab)

    Data Augmentation [ipynb]

16. 2021.05.21 (theory)

    Reinforcement Learning [pdf]

17. 2021.05.21 (lab)

    Object Detection: Inference [ipynb]

18. 2021.05.28 (theory)

    Deep Reinforcement Learning [pdf]

    Importance Sampling [pdf]

19. 2021.06.04 (lab)

    DRL: Actor-Critic [ipynb]

20. 2021.06.11 (theory)

    Alpha Zero [pdf]

Instructor

1. Marco Piastra

2. Contact: marco.piastra@unipv.it

Kiro

1. Course info

Exams

1. See Faculty website

Further resources:

(There are no required textbooks for this course. The following books are recommended as optional readings)

Links

1. Artificial Intelligence Reading Group