Teaching plan for the course unit
Close

 

General information

 

Course unit name: Machine Learning for Classical and Quantum Systems

Course unit code: 574648

Academic year: 2021-2022

Coordinator: Bruno Julia Diaz

Department: Department of Quantum Physics and Astrophysics

Credits: 3

Single program: S

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

26

	-  Lecture	Face-to-face and online		20
	-  Lecture with practical component	Face-to-face and online		6

Independent learning

49

Competences to be gained during study

 

Understanding of architectures and properties of classical neural networks. Understanding methods of learning and learning algorithms. Basic concepts of storage capacity. Introduction to quantum computing and simulations. Quantum neural networks -- models and properties. Practical machine learning - studies of selected methods of supervised and unsupervised machine learning. Interpretability of machine learning methods . Applications of machine learning methods to quantum many body systems.

Learning objectives

Referring to knowledge A. To develop a broad and unified perspective of the properties and  characteristics of neural networks and machine learning systems  B. To become familiar with the basic tools of contemporary machine learning methods  C. To learn machine learning for practical applications

 

 

Teaching blocks

 

1. Classical MC methods with exercise

2. Storage capacity of Neural Networks

3. Perceptron algorithm; Back-propagation algorithm

4. Gardner's program

5. Quantum computers and simulators

6. Quantum neural networks and machine learning

7. Recap - Foundations

8. Deep NN with exercises

9. Interpretability of NN with exercises

10. RMBs with exercise

11. Classical machine learning like reinforcement learning/planning with exercise, computer vision or natural language processing with exercise

12. Final thoughts

 

 

Teaching methods and general organization

 

1. Lectures where theoretical contents of the subject are presented.  2. Practical exercise classes in which students may participate.  3. Activities related to the subject suggested by the teaching staff.

 

 

Official assessment of learning outcomes

 

1. Homework exercises (40 point)  2. A final written examination (20 points)

Reading and study resources

Consulteu la disponibilitat a CERCABIB

Book

 D. Amit, Modelling Brain Function, Cambridge University Press, 2012 (on line)

 M. Lewenstein, A. Sanpera, V. Ahufinger, Utracold Atoms in Opticall Lattices, Oxford  University Press, (2017)