Bibliography
Probabilistic Deep Learning
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- K. P. Murphy, Probabilistic Machine Learning: An Introduction, The MIT Press, 2022
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- J. M. Tomczak, Deep Generative Modeling, Springer, 2022
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Deep Learning
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- A. Zhang, Z. C. Lipton, M. Li, A. J. Smola, Dive into Deep Learning, Cambridge University Press, 2023
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- E. Alpaydin, Introduction to Machine Learning (4th Ed.), The MIT Press, 2020
- I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016
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- Ph. Grohs, G. Kutyniok (editors), Mathematical Aspects of Deep Learning, Cambridge University Press, 2023
- A. Pajankar, A. Joshi, Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch, 2022
Machine Learning
- K. P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
- R. O. Duda, Peter E. Hart and D. G. Stork, Pattern Classification (2nd Ed.), Wiley, 2001
- Ch. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
- S. Haykin, Neural Networks and Learning Machines (3rd Ed.), Prentice Hall, 2009
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Probability & Mathematical Statistics
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- G. Casella, R. L. Berger, Statistical Inference, Duxbury Press, 2001
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