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Data Science introduction from the Ecole polytechnique
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Lab1
Lab2
Lab3
Lab4
Lab5
Lab6
Lab7
Lab8
Lab9
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readme.md

readme.md

Data Scicence Introduction - École polytechnique

Presentation

Here some of the lab assignements and slides from my data science course at the Ecole polytechnique. Code and datasets are too heavy to store here, but assignments + slides have presentations of the techniques.

Their point is not to get into the details, rather broadly mention various machine learning and data science techniques.

Organization

  • Lab1

    • Assignment: Dimensionality Reduction: SVD, PCA, MDS
  • Lab2

    • Assignment: Feature Selection: χ2 Measure Information Gain
    • Slides: Supervised Learning, Naive Bayes, decision trees, regression, χ2 Measure, Information Gain
  • Lab3

    • Assignment: Supervised Learning: k-NN, Adaline
    • Slides: Logistic regression, Perceptron, LDA
  • Lab4

    • Assignment: Supervised Learning: DA, Logistic Regression
    • Slides: Kernels, Intro to Clustering and Hadoop
  • Lab5

    • Assignment: Gaussian Mixture Model
  • Lab6

    • Slides: Random Forests, Adaboost
  • Lab7

    • Assignment: Unsupervised learning: k-Means, Spectral Clustering
    • Slides: Graph Mining
  • Lab8

    • Assignment: Graph Mining and Community detection
    • Slides: Graph Mining
  • Lab9

    • Slides: Introduction to Scala, Introduction to Spark
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