ML/lecture_notes

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  • 📁 images
  • 📄 Sommario Lezioni42 KB
  • 📄 Lecture 01 - Introduction to ML18 KB
  • 📄 Lecture 02 - Fitting of Polynomial I26 KB
  • 📄 Lecture 03 - Probability/Statistics Review30 KB
  • 📄 Lecture 04 - Bayesian Statistics20 KB
  • 📄 Lecture 05 - Information Theory20 KB
  • 📄 Lecture 06 - Model Inference32 KB
  • 📄 Lecture 07 - Fitting of Polynomial II20 KB
  • 📄 Lecture 08 - Linear Regression31 KB
  • 📄 Lecture 09 - Linear Classification I25 KB
  • 📄 Lecture 10 - Linear Classification II49 KB
  • 📄 Lecture 11 - Probabilistic Classification I28 KB
  • 📄 Lecture 12 - Probabilistic Classification II16 KB
  • 📄 Lecture 13 - Probabilistic Classification III20 KB
  • 📄 Lecture 14 - Probabilistic Classification IV17 KB
  • 📄 Lecture 15 - Montecarlo Methods27 KB
  • 📄 Lecture 16 - Non Parametric Models29 KB
  • 📄 Lecture 17 - Kernel Regression9 KB
  • 📄 Lecture 18 - Gaussian Processes23 KB
  • 📄 Lecture 19 - Support Vector Machines I19 KB
  • 📄 Lecture 20 - Support Vector Machines II27 KB
  • 📄 Lecture 21 - Support Vector Machines III24 KB
  • 📄 Lecture 22 - Support Vector Machines IV15 KB
  • 📄 Lecture 23 - Neural Networks I14 KB
  • 📄 Lecture 24 - Neural Networks II28 KB
  • 📄 Lecture 25 - Deep Learning I11 KB
  • 📄 Lecture 26 - Deep Learning II20 KB
  • 📄 Lecture 27 - Deep Learning III19 KB
  • 📄 Lecture 28 - Decision Trees17 KB
  • 📄 Lecture 29 - Ensemble Methods33 KB
  • 📄 Lecture 30 - Principal Component Analysis21 KB
  • 📄 Lecture 31 - Singular Value Decomposition16 KB
  • 📄 Lecture 32 - Clustering I20 KB
  • 📄 Lecture 33 - Clustering II20 KB
  • 📄 Lecture 34 - Clustering III12 KB