Section outline

  • Κύρια textbooks

    • Simon Haykin - Neural Networks and Learning Machines - Prentice Hall, 3rd Edition (2008) - Simon Haykin, Νευρωνικά Δίκτυα και Μηχανική Μάθηση, Εκδόσεις Παπασωτηρίου, 2010
    • Christopher Bishop - Pattern Recognition And Machine Learning - Springer (2006)
    • Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning (second edition) - The MIT Press (2018)
    • Shai Shalev-Shwartz and Shai Ben-David - Understanding Machine Learning: From Theory to Algorithms - Cambridge University Press (2014)

    Συμπληρωματικά textbooks

    • Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Deep learning. MIT press (2016)
    • Vladimir Vapnik - The Nature Of Statistical Learning (second edition) - Springer  (2010)

    Πρακτική Μηχανική Μάθηση

    • Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 3rd Ed. Packt Publishing (2019) - code repository
    • Géron, A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media. (2019) - code repository

    Αντιστοίχιση ύλης - κεφαλαίων textbooks

      Haykin Bishop Mohri Shalev Goodfellow
    Supervised Learning   4 1 2,9  
    Model Selection     4 11  
    Perceptron 1 4 8 9  
    Regression   3 11,6 9,16  
    Clustering   9   22  
    Decision Trees     9 18  
    MLP 4 5   20  
    DFFN         6
    The PAC Learning Framework     2 3,4  
    SVM & Kernel Methods 6 7 5,6 15,16  
    Regularization   5 5 13  
    Rademacher Complexity - VC Dimension     3 26,6  
    Deep Learning Intro1         9
    Online Learning     8 21  
    Reinforcement Learning     17    
    Boosting     7 10  
    Multiclass Classification - Ranking     9,10 17
    1για το ResNet: Dive Into Deep Learning 7.6