Teaching methods
The teaching method is classroom discussion of case studies, and lectures presented by the professor. There are no required texts and no homework – but attendance at seminar sessions is required because the course’s main ideas will be developed collaboratively, through the seminar discussions. AI ethics will be learned by doing AI ethics.

Assessment methods
Students will present a power point / poster presentation. It will be an AI ethics evaluation of an AI application. The AI application may be a tool the student is developing in their own work, or it may be a publicly known artificial intelligence application (ChatGPT, for example, or smart glasses, or Tesla and autopilot). The presentation will last 15 – 20 minutes plus 5 – 10 minutes of questions.
Students will be graded on their ability to locate the ethical dilemmas that arise around AI technology, and their ability to discuss the dilemmas knowledgeably. There are no right or wrong answers in ethics, but there are better and worse understandings of the human values that guide and justify decisions.
Because the main ideas will be developed through classroom discussion, attendance to at least 80% of seminar sessions is required in order to do the final presentation.

Bibliography
The bibliography will be the seminar sessions and the subsequently published decks.
Website: https://trento.ai.ethicsworkshop.org/

References:
Sandholm, William H. Population games and evolutionary dynamics. MIT press, (2010).
Newton, Jonathan. “Evolutionary game theory: A renaissance.” Games 9.2 (2018): 31.
Young, H. Peyton. Individual strategy and social structure: An evolutionary theory of institutions. Princeton University Press, (2001)

Syllabus: Module 1: Spatial and Mobility Data Analysis

Fundamentals of Geographical Information Systems
Geographic coordinates systems
Vector data model
Trajectories
Spatial Tessellations
Flows
Practice: Python packages for geospatial analysis (Shapely, GeoPandas, folium, scikit-mobility)
Digital spatial and mobility data
Mobile Phone Data
GPS data
Social media data
Other data (POIs, Road Networks, etc.)
Practice: reading and exploring spatial and mobility datasets in Python
Preprocessing mobility data
filtering compression
stop detection
trajectory segmentation
trajectory similarity and clustering
Practice: data preprocessing with scikit-mobility

Module 2: Mobility Patterns and Laws

individual mobility laws/patterns
collective mobility laws/patterns
Practice: analyze mobility data with Python

Module 3: Predictive and Generative Models

Prediction
Next-location prediction
Crowd flow prediction
Spatial interpolation
Generation
Trajectory generation
Flow generation
Practice: mobility prediction and generation in Python

Module 4: Applications

Urban segregation models
Routing and navigation apps
Traffic simulation with SUMO

Syllabus: Computational learning tasks for predictions, learning as function approximation, generalization concept.

Basic concepts and models: Continuos and discrete hypothesis space, inductive bias, linear and nearest neighbor models (learning algorithms and properties), regularization.

Neural Networks (NN) architectures and learning algorithms: Perceptron. Multi-layers feedforward models. Deep models. Randomized NN. Recurrent NN. Regularization.

Validation: model selection and model assessment.

Principles of learning processes: Elements of Statistical Learning Theory. Bias/variance analysis.

Support Vector Machines and Kernels-based models.

Unsupervised learning: vector quantization, self-organizing map (SOM).

Introduction to benchmarks and applications.

Introduction to advanced approaches (structured domains, learning on graphs).

Syllabus: The course aims to:

– Introduce the concepts of minimization algorithms for a scalar functional (the loss function).
– Provide the necessary tools for practical course execution, such as Python, Tensorflow, and Pytorch.
– Cover dense neural networks and examples of their applications in physics.
– Explore convolutional neural networks and examples of their applications in physics.
– Discuss recurrent neural networks and examples of their applications in physics.
– Investigate graph neural networks: inductive bias and examples of their applications in physics.
– Examine attention mechanisms: transformers and examples of their applications in physics.
– Study generative neural networks and examples of their applications in physics.
– Provide an overview of differentiable programming.

The course encompasses these topics to provide students with a comprehensive understanding of machine learning algorithms in the context of physics applications.

Syllabus: Prerequisites

The basic courses of the BSc (Laurea Triennale) in Mathematics. Knowledge of linear algebra is particularly important.

Syllabus

Historical introduction; overview of the course

Hilbert spaces and Dirac notation; spectral theorem (no proof); positive semidefinite operators; Hilbert-Schmidt inner product

Density operators; Pauli matrices; the Bloch sphere; unitary evolution; quantum measurements; projective measurements; distinguishing quantum states; quantum observables; compatible observables; single-qubit projective measurements

Tensor product of Hilbert spaces and of linear operators; composite quantum systems; partial measurements; partial trace; reduced density operator; singular value decomposition; Schmidt decomposition

EPR paradox and CHSH inequality

Turing machines; uncomputability of the halting function; Boolean circuits; the strong Church-Turing thesis; decision problems; formal languages; the complexity classes P, BPP, NP and PSPACE; reducibility; NP-complete problems; reversible computation

Quantum circuits; duality between the operator norm and the trace norm; Helstrom bound; Solovay-Kitaev theorem (no proof); CNOT gate; universality of {H,T,CNOT} gates

The complexity class BQP; BPQ is included in PSPACE; quantum parallelism; oracles

Deutsch algorithm; Deutsch-Jozsa algorithm; Bernstein-Vazirani algorithm; Simon’s algorithm

The quantum Fourier transform; quantum phase estimation

The period-finding algorithm; the order-finding algorithm; Shor’s algorithm; the hidden subgroup problem

The one-time pad; public key cryptography; the RSA protocol; Grover’s algorithm; quantum counting algorithm; optimality of quantum counting

Quantum operations; Kraus representation; isometric dilations; bit-flip and phase-flip channels; depolarizing channel

Quantum key distribution; the BB84 protocol

Quantum error correction; classical repetition code; bit-flip and phase-flip codes; Shor’s 9-qubit code