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

BASIC:

  • CB1: Students have demonstrated that they possess and understand knowledge in an area of study that builds on general secondary education, and is typically at a level that, while based on advanced textbooks, also includes some aspects that involve knowledge from the forefront of their field of study.
  • CB3: Students have the ability to gather and interpret relevant data (usually within their area of study) to make judgments that include reflection on relevant social, scientific, or ethical issues.
  • CB4: Students can convey information, ideas, problems, and solutions to both specialized and non-specialized audiences.
  • CB5: Students have developed those learning skills necessary to undertake further studies with a high degree of autonomy.

GENERAL:

  • CG7: Integrate basic concepts related to the field of biomedicine both theoretically and experimentally.
  • CG10: Design, draft, and execute projects related to the area of Biomedical Sciences.
  • CG11: Recognize basic concepts from different areas linked to biomedical sciences.

SPECIFIC:

  • CE7: Apply statistical tools to studies in Health Sciences.
  • CE19: Recognize the principles of biomedical sciences related to health and learn to work in any area of Biomedical Sciences (biomedical company, bioinformatics laboratories, research laboratories, clinical analysis company, etc.).

CROSS-CURRICULAR:

  • CT1: Develop the capacity for organization and planning appropriate to the moment.
  • CT2: Develop the capacity for problem-solving.
  • CT3: Develop the capacity for analysis and synthesis.
  • CT4: Interpret experimental results and identify consistent and inconsistent elements.
  • CT5: Use the internet as a means of communication and as a source of information.
  • CT6: Know how to communicate, make presentations, and write scientific papers.
  • CT7: Be able to work in a team.
  • CT8: Reason and evaluate situations and results from a critical and constructive point of view.
  • CT9: Have the ability to develop skills in interpersonal relationships.
  • CT10: Be capable of carrying out autonomous learning.
  • CT11: Apply theoretical knowledge to practice.
  • CT12: Apply the scientific method.

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.
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