Statistical Learning

• Teachers
• Weekly hours
• Learning Outcomes
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Santiago Marco Colás ( )

Others

• Agustín Gutiérrez Gálvez ( )
• Elitza Nikolaeva Maneva ( )

Weekly hours

Learning Outcomes

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. Implement correct data partition schemes for training, optimization and performance assessment in predictive modeling.
   Related competences: K2, K3, K4, S3, S4,
2. Select proper data preprocessing techniques before model building
   Related competences: K2, K3, K4,
3. Perform dimensionality reduction using both feature selection and extraction methods.
   Related competences: C3, C6, K2, K3, K4, K5, S3, S4,
4. Critically assess model performance using appropriate validation techniques.
   Related competences: C3, C6, K2, K3, K4, S3, S8,
5. Apply advanced machine learning and signal processing methods to real-world bioinformatics and health data challenges.
   Related competences: C3, C6, K2, K3, K4, K5, S2, S3, S4, S8,
6. To write a lab report in formal language, good structure and good quality visuals
   Related competences: C3,
7. Orally defend a teamwork regarding a machine learning analysis of a dataset. Produce good-quality slides and structure the presentation to provide a clear message to the audience. Answer technical questions with proficiency.
   Related competences: C3, S8,
8. Understand technical literature in the area of statistical learning for health. Identify key concepts and identify ideas that required deeper analysis.
   Related competences: K2, C6, K3,

Contents

1. Introduction to Statistical Learning: Basic Concepts and Examples
   Motivation and basic concepts: Application examples. Tools
2. Introduction to Statistical Learning (II)
   Figures of merit. Basic Classifiers. Overfitting i complexity control. Dimensionality reduction. Regularization.
3. Data preprocessing: from raw data to features.
   Examples in spectrometry. Noise reduction, baseline correction, peak detection and integration, alignment, non-linear transformations, scaling and normalization techniques.
4. Dimensionality reduction: Feature extraction
   The curse of dimensionality. Principal Component Analysis. Linear Discriminant Analysis.
5. Dimensionality Reduction: Feature Selection
   The importance of data partition. Univariate approaches. Mutivariate approches: Filters, Wrappers, Sequential Searches, Genetic Algorithms. Feature Rankings and Recursive Feature Elimination.
6. Clustering
   K-means, Hierarchical clustering, Gaussian Mixture Models, Parzen Windows
7. Basic classifiers
   Bayes Theorem. Linear and Quadratic Discriminant Classifiers. Naive Bayes. Partial Least Squares Discriminant Analysis.
8. Model validation and cross-validation
   Validation levels and purpose. Estratificació. Internal/External validation. Hold-out, Leave-one-out, k-fold, random subsampling, Bootstrap
9. Advanced classifiers
   Support Vector Machines, Decision Trees, Random Forest. XGBoost
10. Multilinear Regression
    Overview of univariable linear regression. Multilinear Regression. The condition number. Ridge Regression. LASSO. Subset selection.
11. Advanced Regression
    Neural Networks, The perceptron. The multilayer perceptron. Gradient descent techniques. Deep Learning. Support Vector Regression.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• The Elements of statistical learning : data mining, inference, and prediction - Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome, Springer, cop. 2009. ISBN: 9780387848570
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003549679706711&context=L&vid=34CSUC_UPC:VU1
• Pattern recognition and machine learning - Bishop, Christopher M, Springer, cop. 2006. ISBN: 9780387310732
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003157379706711&context=L&vid=34CSUC_UPC:VU1
• Bioinformatics : the machine learning approach - Baldi, Pierre; Brunak, Soren, The MIT Press, cop. 2001. ISBN: 9780262025065
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003149339706711&context=L&vid=34CSUC_UPC:VU1
• Multidimensional scaling - Cox, Trevor F; Cox, Michael A. A, Chapman & Hall, cop. 2001. ISBN: 9781584880943
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003165429706711&context=L&vid=34CSUC_UPC:VU1
• Correspondence analysis in practice - Greenacre, Michael J, Academic press, cop. 1993. ISBN: 9780122990526
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991000916169706711&context=L&vid=34CSUC_UPC:VU1
• Chemometrics with R: multivariate data analysis in the natural sciences and life sciences - Wehrens, Ron, Springer Science, 2011.
  
• Introduction to multivariate statistical analysis in chemometrics - Varmuza, Kurt; Filzmoser, Peter, CRC Press, 2016.
  
• Data Science and Predictive Analytics: Biomedical and Health Applications using R - Dinov, Ivo D., Springer, 2018.
  
• Modern statistics for modern biology - Holmes, Susan; Huber, Wolfgang, Cambridge Press, 2025.
  
• An Introduction to statistical learning : with applications in R - James, Gareth, ©2021. ISBN: 9781071614174
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004922348606711&context=L&vid=34CSUC_UPC:VU1
• An introduction to statistical Learning with applications in Python - James, Gareth, Springer,
  

Previous capacities