CS-E4810 - Machine Learning and Neural Networks, 31.10.2016-09.12.2016

Lectures

• Lectures
  

  Lectures

  • Lecture 1: Introduction to Neural Networks Fil PDF document
  • Lecture 1, black-and-white version Fil PDF document
  • Lecture 2: Simple Neural Models Fil PDF document
  • Lecture 2, black-and-white version Fil PDF document
  • Lecture 3: Data Preprocessing and Principal Component Analysis Fil PDF document
  • Lecture 3, black-and-white version Fil PDF document
  • Lecture 4: Multilayer Perceptron Networks and Backpropagation Algorithms Fil PDF document
  • Lecture 4, black-and-white version Fil PDF document
  • Lecture 5: Improved Learning Methods for Multilayer Perceptron Networks Fil PDF document
  • Lecture 5, black-and-white version Fil PDF document
  • Lecture 6: Model Assesment and Selection Fil PDF document
  • Lecture 6, black-and-white version Fil PDF document
  • Lecture 7: Radial Basis Function Networks Fil PDF document
  • Lecture 7, black-and-white version Fil PDF document
  • Lecture 8: Support Vector Machines Fil PDF document
  • Lecture 8, black-and-white version Fil PDF document
  • Lecture 9: Independent Component Analysis Fil PDF document
  • Lecture 9, black-and-white version Fil PDF document
  • Lecture 10: Self-Organizing Maps and Learning Vector Quantization Fil PDF document
  • Lecture 10, black-and-white version Fil PDF document
  • Lecture 11: Processing of Temporal Information Fil PDF document
  • Lecture 11, black-and-white version Fil PDF document
  • Lecture 12: Combining Multiple Classifiers Fil PDF document
  • Lecture 12, black-and-white version Fil PDF document
  • Lecture 13: Deep Learning Fil PDF document
  • Lecture 13, black-and-white version Fil PDF document