FONDAMENTI DI ANALISI DATI E LABORATORIO

Academic Year 2020/2021 - 1° Year - Curriculum Data Science
Teaching Staff Credit Value: 9
Scientific field: INF/01 - Informatics
Taught classes: 36 hours
Exercise: 24 hours
Laboratories: 12 hours
Term / Semester:

Learning Objectives

  • FONDAMENTI DI ANALISI DATI

    Basic introduction to state of the art data analysis and automated classification.

  • Fundamental of Data Analysis

    Basic introduction to state of the rt data analysis and automated classification.


Course Structure

  • FONDAMENTI DI ANALISI DATI

    Frontal lectures in classroom

    Modalities may change if requirtement by objective conditions (COVID 19)

  • Fundamental of Data Analysis

    Live lecture in presence (whenver possible)
    Interactive labs.


Detailed Course Content

  • FONDAMENTI DI ANALISI DATI
    • Fundamental of Data Analysis

      Data visualization, descriptive statistics

      regression and correlation. Logistic regression.

      Bayes approach to learning. MAP.

      TS, CS, trainign and generalization error. Confuson matrix. ROC.

      LDA, SVM.

      Kernel trick: non linear SVM

      PCA, non linear techniques for dimension reduction

      K-nn

      CART.

      Clustering: k-means and hierarchical clustering.

      Ensblem techniques. Boosting

  • Fundamental of Data Analysis

    Data visualization, descriptive statistics

    regression and correlation. Logistic regression.

    Bayes approach to learning. MAP.

    TS, CS, trainign and generalization error. Confuson matrix. ROC.

    LDA, SVM.

    Kernel trick: non linear SVM

    PCA, non linear techniques for dimension reduction

    K-nn

    CART.

    Clustering: k-means and hierarchical clustering.

    Ensblem techniques. Boosting


Textbook Information

  • FONDAMENTI DI ANALISI DATI

    a) Chapters from: Pattern Recognition and Machine Learning (Information Science and Statistics) Bishop C.M: Editore: Springer, 2007

    b) Chapters from:Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython (Inglese) W.Mckinney O'reilly 2017

    Tutorial on line, handsout

  • Fundamental of Data Analysis

    Teacher's handouts

    Chapters from:Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython (Inglese) W.Mckinney O'reilly 2017

    Jupyter notebooks