Ateneo - UPO

"Introduction to the main algorithms and data structures for the retrieval, within a large collection, of the documents that satisfy an information need. 
Introduction to the main network science models and data driven analytical tools. Network science is a discipline that allows to study a complex system represented by means of a network (like a social network, a communication network, a contact network, and so on)."

"[ir] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008

[ns] F. Menczer, S. Fortunato & C. Davis, A First Course in Network Science, Cambridge University Press. 2020

Additional material provided by the teacher"

"The purpose of the course is threefold. First, students will learn what an information retrieval (IR) system is, what are the basic techniques for designing an efficient and scalable IR system on large collections of documents, the main applications (for example, how to build a web search engine) and future directions.
Secondly, they will learn the fundamental principles of network science (NS), including the analysis of complex networks, the basic models for studying network dynamics (e.g., social contagion, viral phenomena, spread of epidemics, etc.).
Finally, it will be explained how network science can be used to design modern information retrieval systems."

Advanced courses on Computer Programming and Algorithms; basics on linear algebra, probability theory and statistics.

"Lectures that introduce theoretical concepts and laboratory exercises that apply them. 

In the lessons, theoretical topics are addressed through slide presentations, with examples and some questions to verify students' learning. 

In laboratory exercises, students are guided in the implementation of simple projects aimed at putting into practice the theoretical knowledge acquired."

Monitoring the learning process: during the course the students will interact with the teacher to solve exercises and lab assignments.

"The exam consists of three parts: (a) the presentation of an in-depth study of your choice, (b) the discussion of an implementation project of your choice, (c) scattered questions about the program. The complete examination of all its parts takes place in a single day. It is possible to prepare the presentation and the project in a group (of 2/3 students maximum).
If the presentation delves into a topic in the information retrieval module, then the project must solve a practical network science problem - or vice versa. A list of sources and topics that can be chosen will be provided by the teacher during the course.
The vote will be expressed out of thirty and will represent the summary of the evaluation of the three parts mentioned above."

"Information Retrieval: 
Postings lists; 
Index construction and compression; 
Vector space model. Relevance scores; Evaluation of information retrieval systems;
Text classification;
Clustering techniques; 
Web search basics;
Web crawling and indexes;
Link analysis.

Word and Document embedding. Network Science: 
Introduction to networks Graph Theory;
Small worlds;
Hubs;
Directed and Weighted networks;
Network models;
Community detection;
Network Dynamics."

"Knowledge and understanding: basic algorithms and data structures for Information Retrieval and Complex Networks Analysis.
Applying knowledge and understanding: students will be able to build and use a basic information retrieval system, as well as analyzing networked data, using available open source tools.
Making judgements: students will be able to autonomously evaluate the best techniques to solve information retrieval and network science problems. 
Communication skills: students will learn to communicate and justify the use of the appropriate technique for a given problem. 
Learning skills: students will be able to autonomously learn how to refine basic techniques and how to best use the available tools."