diff --git a/README.md b/README.md index ef1b8053c..cbd4ae790 100644 --- a/README.md +++ b/README.md @@ -216,9 +216,10 @@ Here we list several useful Python libraries we strongly recommend (if you use a ## 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. +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, Murphy 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 good extent this text. +- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html The weekly plans will include reading suggestions from these two textbooks. In addition, you may find the following textbooks interesting. _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. @@ -236,18 +237,18 @@ _General Machine Learning Books_: - 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. +## 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. -- _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. +- _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/ @@ -256,5 +257,8 @@ The link here https://www.mn.uio.no/english/research/about/centre-focus/innovati + + +