0590

Freie Universität Berlin (FU Berlin)

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Machine Learning for Bioinformatics
  • Zusammenfassung

    Machine Learning for Bioinformatics

  • This document outlines foundational and advanced machine learning concepts tailored for bioinformatics. It begins by contrasting supervised methods, such as linear regression and Naive Bayes, with unsupervised techniques like K-means clustering and expectation maximization. The text then progresses to complex probabilistic structures, including Hidden Markov Models and Bayesian Networks. A significant portion explores deep learning, detailing artificial neural networks, convolutional networks, a...
  • silviacambiago
    8,16 € Mehr Infos
Machine Learning for Data Science
  • Notizen

    Machine Learning for Data Science

  • his comprehensive document provides an extensive overview of fundamental machine learning and modern data science concepts. It clearly contrasts modern data-driven approaches with classical hypothesis-driven experimentation. Key topics include essential data preprocessing and visualization techniques, emphasizing dimensionality reduction methods like PCA, MDS, and t-SNE. The text deeply explores anomaly detection and clustering algorithms, such as K-means and Spectral Clustering. It also covers ...
  • silviacambiago
    8,96 € Mehr Infos
DBMS for Data Science
  • Notizen

    DBMS for Data Science

  • This document provides a comprehensive overview of Database Management Systems for data science. It contrasts file systems with databases, introducing schemas, instances, and architecture. It covers relational design, SQL, functional dependencies, and normalization to prevent anomalies. The text explores analytical processing via OLAP, data warehousing, and multidimensional cubes. Additionally, it details physical storage, B+-tree indexing, and transaction management, emphasizing ACID properties...
  • silviacambiago
    8,16 € Mehr Infos
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