Universitat Internacional de Catalunya

Artificial Intelligence I

Artificial Intelligence I
4
14873
4
Second semester
op
Main language of instruction: English

Other languages of instruction: Catalan, Spanish

Teaching staff


Dra. ÁLVAREZ DE LA CAMPA CRESPO, Elena - ealvarezdelacampa@uic.es

Introduction

Artificial Intelligence (AI) is generating significant interest in bioinformatics research due to its multiple applications, particularly in biomedical-clinical settings. This subject aims to familiarize students with the most basic concepts of AI, so that they can understand its practical applications and learn how to develop their own AI projects. To achieve this, we will start from scratch and cover the main technical steps in building AI predictors: (i) data collection; (ii) selection of the predictor; (iii) validation of results; and (iv) controlled deployment of AI programs.

Pre-course requirements

It is recommended to have completed and passed:

  • Introduction to Bioinformatics

It is recommended to take in parallel:

  • Artificial Intelligence I
  • Programming Knowledge

Objectives

  • Understand the approach of artificial intelligence to scientific-technological problems. 
  • Know and understand the basic techniques of artificial intelligence.

Competences/Learning outcomes of the degree programme

  • CB01 - Students must demonstrate that they have and understand knowledge in an area of study that is based on general secondary education, and it tends to be found at a level that, although it is based on advanced textbooks, also includes some aspects that involve knowledge from the cutting-edge of their field of study.
  • CB03 - Students must have the ability to bring together and interpret significant data (normally within their area of study) to issue judgements that include a reflection on significant issues of a social, scientific and ethical nature.
  • CB04 - That students can transmit information, ideas, problems and solutions to specialist and non-specialist audiences.
  • CB05 - That students have developed the necessary learning skills to undertake subsequent studies with a high degree of autonomy.
  • CE07 - To apply statistical tools to Health Science studies.
  • CE19 - To be aware of the principles of biomedical science related to health and learn how to work in any field of Biomedical Sciences (biomedical companies, bioinformatics laboratories, research laboratories, clinical analysis companies, etc.).
  • CG07 - To incorporate basic concepts related to the field of biomedicine both at a theoretical and an experimental level.
  • CG10 - To design, write up and execute projects connected to the field of Biomedical Sciences.
  • CG11 - To be aware of basic concepts from different fields connected to biomedical sciences.
  • CT01 - To develop the organisational and planning skills that are suitable in each moment.
  • CT02 - To develop the ability to resolve problems.
  • CT03 - To develop analytical and summarising skills.
  • CT04 - To interpret experimental results and identify consistent and inconsistent elements.
  • CT05 - To use the internet as a means of communication and a source of information.
  • CT06 - To know how to communicate, give presentations and write up scientific reports.
  • CT07 - To be capable of working in a team.
  • CT08 - To reason and evaluate situations and results from a critical and constructive point of view.
  • CT09 - To have the ability to develop interpersonal skills.
  • CT10 - To be capable of autonomous learning.
  • CT11 - To apply theoretical knowledge to practice.
  • CT12 - To apply scientific method.
  • CT13 - To be aware of the general and specific aspects related to the field of nutrition and ageing.
  • CT14 - To respect the fundamental rights of equality between men and women, and the promotion of human rights and the values that are specific to a culture of peace and democratic values.

Learning outcomes of the subject

As a specific learning outcome of this program, the following is contemplated:

  • The student understands and internalizes the basic elements of Artificial Intelligence required in biomedical projects.

Syllabus

1. Artificial Intelligence Applied to Biomedical Problems:

  • Overview of AI in the biomedical field. 
  • Practical examples of AI applications in biomedicine.
  1. Gentle Introduction to the Mathematics Behind AI:
  • Basic concepts of AI and machine learning. 
  • A Model for Every Problem: Examples of biomedical problems and how AI can address them. 
  • Binary Classification Tasks: Understanding the concept of classification and how it's used in AI. 
  • Regression: Continuous prediction problems in biomedicine.
  • Introduction to the basic concepts of AI and machine learning, focusing on biomedical applications. Includes examples of biomedical problems and how they can be addressed with different AI techniques.
  1. From the Real World to the World of AI: What to Consider When Planning the Use of AI in Your Research?:
  • Data collection and its importance. 
  • How to select the right problem for AI use. 
  • Ethical and legal aspects to consider. 
  • We will delve into how to select a suitable biomedical problem to apply AI, how to collect and prepare the necessary data, and how to face ethical and legal challenges in this process.
  1. The Main Steps in the Development of AI Models:
  • Data preparation and cleaning. 
  • Choosing Discriminative Properties: Understanding features and how to select them. 
  • Finding a Suitable Model: Introduction to Random Forest, Neural Networks, etc. 
  • Validating Your Predictor: How to ensure the reliability of your model. 
  • Students will learn how to prepare and process data for AI, how to select the most relevant features, and how to choose the appropriate AI model (Random Forest, Neural Networks, etc.). Techniques for validating AI models will be explored, ensuring they are accurate and reliable for use in biomedical applications.
  1. AI in the Real World: An Overview of Deployment Guidelines
  • How is an AI model implemented in a real environment? 
  • Guidelines to ensure a successful and ethical implementation. 
  • Discussion on the current limitations and challenges of AI in biomedicine.

Teaching and learning activities

In person



  • Lectures in blocks of between 15 and 50 minutes on a theoretical topic to be developed by the professor.
  • Clinical cases or case methods (CM): Presentation of a real or imaginary situation. Students work on the questions formulated in small groups or in active interaction with the teacher and the answers are discussed. The teacher intervenes actively and, if necessary, contributes new knowledge.
  • Virtual education (VE): Online material that students can consult from any computer at any time and that will contribute to self-learning of concepts related to the course.

Evaluation systems and criteria

In person



  • Students in the first call:
    • Continuous Assessment (35%): Includes practical exercises and short tests. 
    • Final Theoretical Exam (65%): Assessment of theoretical knowledge and understanding of the AI II concepts applied to biomedicine covered during the course. 
    • Subjective Component (up to 10%): Up to 10% of the final grade may be allocated based on subjective criteria such as engagement, participation, and adherence to rules, to encourage an active and committed attitude in the classroom. 
  • Students in the second or subsequent call: The Continuous Assessment grade is retained, and the final exam will account for 75% of the final grade. 

General points to consider about the evaluation system:

  • To calculate an average grade, a minimum score of 5 is required in the final exam. 
  • In addition to the above, to pass the course, the average of all grades must be 5 or higher. 
  • The ongoing nature of this assessment means that it is not possible to evaluate the subject if participation in 75% of the hours has not been achieved. 
  • Misuse of electronic devices (such as recording and distributing content of students or teachers during sessions, as well as using these devices for non-educational purposes) can lead to expulsion from the class.

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:
  • E2 26/06/2024 A04 18:00h
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