WMR/lecture_notes

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  • 📄 Lecture Summary30 KB
  • 📄 Lecture 01 - Introduction to WMR14 KB
  • 📄 Lecture 02 - Maching Learning Methods29 KB
  • 📄 Lecture 03 - Geometric Models I47 KB
  • 📄 Lecture 04 - Geometric Models II21 KB
  • 📄 Lecture 05 - Naive Bayes Classifier I31 KB
  • 📄 Lecture 06 - Naive Bayes Classifier II32 KB
  • 📄 Lecture 07 - Machine Learning Metrics37 KB
  • 📄 Lecture 08 - Natural Language Processing I17 KB
  • 📄 Lecture 09 - Lab I22 KB
  • 📄 Lecture 10 - Natural Language Processing II18 KB
  • 📄 Lecture 11 - Natural Language Processing III23 KB
  • 📄 Lecture 12 - Lab II23 KB
  • 📄 Lecture 13 - Hidden Markov Model I25 KB
  • 📄 Lecture 14 - Hidden Markov Model II27 KB
  • 📄 Lecture 15 - Hidden Markov Model III21 KB
  • 📄 Lecture 16 - PAC Learnability23 KB
  • 📄 Lecture 17 - Model Selection18 KB
  • 📄 Lecture 18 - Support Vector Machine I22 KB
  • 📄 Lecture 19 - Support Vector Machine II17 KB
  • 📄 Lecture 20 - Clustering27 KB
  • 📄 Lecture 21 - Kernel Methods I18 KB
  • 📄 Lecture 22 - Kernel Methods II10 KB
  • 📄 Lecture 23 - Lab III11 KB
  • 📄 Lecture 24 - Neural Networks I18 KB
  • 📄 Lecture 25 - Lab IV13 KB