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 electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
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
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 larg extent this text. The weekly plans will include reading suggestions from these two textbooks. Additional textbooks:
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¶
The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.
STK2100 Machine learning and statistical methods for prediction and classification http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html.
IN3050 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.
STK-INF3000/4000 Selected Topics in Data Science http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html. The course provides insight into selected contemporary relevant topics within Data Science.
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.
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.
INF4490 Biologically Inspired Computing http://www.uio.no/studier/emner/matnat/ifi/INF4490/. An introduction to self-adapting methods also called artificial intelligence or machine learning.
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.
IN5400/INF5860 Machine Learning for Image Analysis https://www.uio.no/studier/emner/matnat/ifi/IN5400/. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
TEK5040 Deep learning for autonomous systems https://www.uio.no/studier/emner/matnat/its/TEK5040/. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
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/