Computational Methods and Techniques for Historical and Cultural Studies
A.Y. 2024/2025
Learning objectives
The course deals with emerging issues in the so-called Digital and Computational Humanities and aims to introduce students to the exploitation of the potential of databases, Linked Open Data technologies and the Semantic Web by exploring solutions oriented to research, study and communication in the historical and cultural fields. In particular, the course intends to provide knowledge and skills in the discipline of computer science with particular reference to technologies for the design and management of digital archives in structured form (databases) and their use in the Web for the representation and exploration of historical data. Additional topics covered in the course concern the use of languages for the web of data with the aim of fostering the integration of documentary archives in the cultural-historical field through digital platforms based on formalisms and technologies related to Linked Open Data and Semantic Web. The course also aims to foster the development of skills related to the design of communication objects and tools essential for fruitful interaction with scholars and IT professionals.
Expected learning outcomes
Knowledge:
- Data modeling and database management
- Notions of conceptual and logical design of databases
- Data web languages, Linked Open Data and Semantic Web
- Data exploration and visualization
Skills:
- Conceptual and logical design of a database
- Creating, populating, and querying databases
- Creating annotations for the data web and Linked Open Data
- Using tools for exploration and visualization of data on the web
- Data modeling and database management
- Notions of conceptual and logical design of databases
- Data web languages, Linked Open Data and Semantic Web
- Data exploration and visualization
Skills:
- Conceptual and logical design of a database
- Creating, populating, and querying databases
- Creating annotations for the data web and Linked Open Data
- Using tools for exploration and visualization of data on the web
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
The teaching is organized into 2 parts.
Part A covers the following topics:
- Introduction to Computer Methodologies in the Humanities: This section includes an overview of Digital Humanities (DH), covering its definition, data types, and relevant project examples and resources. Additionally, it explores the intersection of DH and Artificial Intelligence (AI), introduces key concepts from information theory, and discusses various data encoding methods.
- Programming Languages Applied to the Social Sciences: This section will introduce Python programming, focusing on fundamental concepts relevant to the needs of the Humanities.
- Machine Learning: This section will cover the fundamental principles of machine learning, including different types of learning (supervised, unsupervised, and reinforcement), and key concepts such as classification, regression, and clustering. It will also involve the practical application of machine learning methods using Python.
- Principles and Tools for Deep Learning and, CNNs Computer Visionand Image Analysis: This section will cover the foundational principles of deep learning, including Convolutional Neural Networks (CNNs), and their applications in image analysis of humanities and cultural data.
Part B covers the following topics:
- Databases and Semantic web: This section introduces the principles of the Semantic Web and database management systems.
- Applied Projects in Humanities Studies: This section emphasizes hands-on applications of computational methods in cultural and historical research using Python. It also covers off-the-shelf tools for digital humanities research.
Part A covers the following topics:
- Introduction to Computer Methodologies in the Humanities: This section includes an overview of Digital Humanities (DH), covering its definition, data types, and relevant project examples and resources. Additionally, it explores the intersection of DH and Artificial Intelligence (AI), introduces key concepts from information theory, and discusses various data encoding methods.
- Programming Languages Applied to the Social Sciences: This section will introduce Python programming, focusing on fundamental concepts relevant to the needs of the Humanities.
- Machine Learning: This section will cover the fundamental principles of machine learning, including different types of learning (supervised, unsupervised, and reinforcement), and key concepts such as classification, regression, and clustering. It will also involve the practical application of machine learning methods using Python.
- Principles and Tools for Deep Learning and, CNNs Computer Visionand Image Analysis: This section will cover the foundational principles of deep learning, including Convolutional Neural Networks (CNNs), and their applications in image analysis of humanities and cultural data.
Part B covers the following topics:
- Databases and Semantic web: This section introduces the principles of the Semantic Web and database management systems.
- Applied Projects in Humanities Studies: This section emphasizes hands-on applications of computational methods in cultural and historical research using Python. It also covers off-the-shelf tools for digital humanities research.
Prerequisites for admission
It would be beneficial to have completed a basic computer science course and to have fundamental knowledge of Python programming, Colab, and statistics for a better understanding of the proposed content; however, it is not mandatory.
Teaching methods
The course topics will be addressed through technical lectures and laboratory sessions, with the help of slides and teaching materials that the lecturer will make available on the myAriel platform. Specific space is reserved for practical case studies aimed at illustrating the concrete application and developing own experience of the theoretical content covered during the course of the lectures.
Attendance, although not mandatory, is strongly recommended. Students unable to attend are advised to contact the lecturer.
Attendance, although not mandatory, is strongly recommended. Students unable to attend are advised to contact the lecturer.
Teaching Resources
- Slides, handouts, scholarly articles provided by the instructor.
- Brian Kokensparger, 2018, Guide to Programming for the Digital Humanities: Lessons for Introductory Python
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning (Adaptive Computation and Machine Learning series) , online: https://www.deeplearningbook.org/
- David G. Stork, 2023, Pixels & Paintings: Foundations of Computer-assisted Connoisseurship
- Brian Kokensparger, 2018, Guide to Programming for the Digital Humanities: Lessons for Introductory Python
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning (Adaptive Computation and Machine Learning series) , online: https://www.deeplearningbook.org/
- David G. Stork, 2023, Pixels & Paintings: Foundations of Computer-assisted Connoisseurship
Assessment methods and Criteria
Attending and non-attending students: verification of learning consists of a written test on the syllabus of Parts A and B consisting of quizzes including exercises and open questions. The evaluation parameters are the ability to clearly present knowledge, proficiency in the use of specialized terminology, completeness of answers, correctness of reasoning in the performance of exercises. The in-depth assessment will be a written exam that includes the submission of a report. The evaluation will be expressed on a scale of thirty and will summarize the results achieved in both the quizzes and the final written exam.
Educational website(s)
Professor(s)