Machine Learning

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
ESAII
This course explores the application of deep learning techniques to bioinformatics and bioengineering, focusing on the unique challenges and opportunities presented by biological and biomedical data. Building on prior knowledge of classical machine learning, students will delve into advanced neural network architectures including multilayer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course covers key domains such as biomedical image analysis, time-series and sequence modeling, generative modeling with VAEs and GANs, and the use of language models for both biomedical texts and biological sequences.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K4 - Integrate the concepts offered by the most widely used programming languages in the field of Life Sciences to model and optimize data structures and build efficient algorithms, relating them to each other and to their application cases.
  • K5 - Identify the nature of the biological variables that need to be analyzed, as well as the mathematical models, algorithms, and statistical tests appropriate to develop and evaluate statistical analyses and computational tools.

    • Skills

  • S2 - Computationally analyze DNA, RNA and protein sequences, including comparative genome analyses, using computation, mathematics and statistics as basic tools of bioinformatics.
  • S3 - Solve problems in the fields of molecular biology, genomics, medical research and population genetics by applying statistical and computational methods and mathematical models.
  • S4 - Develop specific tools that enable solving problems on the interpretation of biological and biomedical data, including complex visualizations.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

    • Competences

  • C3 - Communicate orally and in writing with others in the English language about learning, thinking and decision making outcomes.
  • C6 - Detect deficiencies in the own knowledge and overcome them through critical reflection and the choice of the best action to expand this knowledge.

    • Objectives

    1. Apply deep learning models to biological and biomedical data, selecting and adapting architectures such as convolutional, recurrent, and transformer networks to solve specific problems in bioinformatics and bioengineering.
      Preprocess, represent, and analyze heterogeneous biomedical data (medical images, biological sequences, physiological signals), using modern computational tools and good scientific programming practices.
      Interpret and evaluate the performance of deep learning models in biomedical contexts, using appropriate metrics and understanding the limitations and ethical risks of using artificial intelligence in biomedicine.
      Related competences: K2, K3, S2, S3, S4, C6,
    2. Preprocess, represent, and analyze heterogeneous biomedical data (medical images, biological sequences, physiological signals), using modern computational tools and good scientific programming practices.
      Related competences: K4, K5, S4, S8, C3, C6,
    3. Interpret and evaluate the performance of deep learning models in biomedical contexts, using appropriate metrics and understanding the limitations and ethical risks of using artificial intelligence in biomedicine.
      Related competences: K4, K5, S4, S8, C3,

    Contents

    1. Introduction to Deep Learning in Bioinformatics & Bioengineering
      Introduction to Deep Learning in Bioinformatics & Bioengineering, Biological Data Types and software tools for effective learning workflows.
    2. Multilayer Perceptrons: Foundations of Deep Learning
      An introduction to the fundamental building blocks of deep learning through multilayer perceptrons (MLPs), covering network architecture, activation functions, forward and backward propagation, and their role in modeling complex nonlinear relationships in biological data.
    3. Biomedical Image Analysis with CNNs
      This module explores convolutional neural networks (CNNs) for analyzing biomedical images, focusing on feature extraction, classification, and segmentation tasks in applications such as pathology, radiology, and microscopy.
    4. Biological Time-Series and Sequence Modeling with RNNs
      An in-depth look at recurrent neural networks (RNNs) and their variants for modeling sequential and temporal biological data, including time series, physiological signals, and nucleotide or protein sequences.
    5. Transformers and Attention Mechanisms for Biological Sequences
      Introduction to the attention mechanisms and transformer architectures, emphasizing their effectiveness in capturing long-range dependencies in biological sequences for tasks such as structure prediction, and functional annotation
    6. Encoder-Decoder Architectures
      Design and application of encoder-decoder models for transforming, annotating, and generating biological sequences and biomedical data, with emphasis on RNN and transformer variants, attention integration, and real-world sequence modeling tasks.
    7. Language Models and NLP in Bioinformatics and Biomedicine
      This module explores how language models are applied to biomedical texts and biological sequences, enabling tasks such as named entity recognition, document classification, and functional annotation through pretrained models like BioBERT, DNABERT, and protein sequence transformers.

    Activities

    Activity Evaluation act


    Theoretical expository lectures


    Objectives: 1 2
    Theory
    25h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    45h

    Laboratories


    Objectives: 2 3
    Theory
    0h
    Problems
    30h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    45h

    Mid Term



    Week: 8
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Theory exam



    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    Lectures will be mainly of expository type. There will be alsolife sessions and practical sessions using python.

    Evaluation methodology

    The course assessment is as follows:
    - 30% corresponds to practical assignments (to be done by pairs),
    - 70% consists of a 2 partial theoretical exams taken at mid term (35%) and final term (35%).

    Recuperation Information
    can be retake.
    Only the students that after the evaluation have a grade equal or greater than 3 can perform the re-evaluation exam. In the reassessment exam (R) only the theoretical part is reassessed and the reassessment grade in this case will be 70%R plus 30% of the practical work carried out during the course

    Bibliography

    Basic

    • Deep Learning for the Life Sciences - Bharath Ramsundar, Peter Eastman, Pat Walters, Vijay Pande, O'Reilly Media, Inc., ISBN: 9781492039839
    • Bioinformatics with Python Cookbook - Tiago Antão, ISBN: 978-1789344691

    Previous capacities

    Basic Python