Textbooks#
Recommended textbooks: The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text and the next which is
Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili (RLM), Machine Learning with PyTorch and Scikit-Learn at https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312, see also https://sebastianraschka.com/blog/2022/ml-pytorch-book.html
The weekly plans will include reading suggestions from the above textbooks. Additional textbooks:
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O’Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
General learning book on statistical analysis:
Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
Peter Hoff, A first course in Bayesian statistical models, Springer
General Machine Learning Books:
Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
Links to relevant courses at the University of Oslo#
FYS5429 Advanced Machine Learning for the Physical Sciences https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html
FYS5419 Quantum Computing and Quantum Machine Learning https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html
STK2100 Machine learning and statistical methods for prediction and classification http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html.
IN3050/4050 Introduction to Artificial Intelligence and Machine Learning https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html. Introductory course in machine learning and AI with an algorithmic approach.
IN4080 Natural Language Processing https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html. Probabilistic and machine learning techniques applied to natural language processing.
IN5550 – Neural Methods in Natural Language Processing https://www.uio.no/studier/emner/matnat/ifi/IN5550/index.html. This course studies a selection of advanced techniques in Natural Language Processing (NLP), with particular emphasis on recent and current research literature. The focus will be on machine learning and specifically deep neural network approaches to the automated analysis of natural language text.
STK-IN4300 Statistical learning methods in Data Science https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
IN-STK5000 Adaptive Methods for Data-Based Decision Making https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html. Methods for adaptive collection and processing of data based on machine learning techniques.
IN4310 Deep Learning for Image Analysis https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
STK4051 Computational Statistics https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html
STK4021 Applied Bayesian Analysis and Numerical Methods https://www.uio.no/studier/emner/matnat/math/STK4021/