Start watching this or first slides here
Part 2: Topics in supervised learning
Syllabus (more to be filled in)
Jurafsky and Martin, Speech and language processing, 3rd ed. draft, Oct. 2019
- Ch. 5, sec. 5.5 Regularization
- except the last paragraph starting with "Both L1 and L2..."
- Ch. 4, sec 4.7 "Evaluation: Precision, Recall, F-measure"
- Ch. 4, sec 4.8 "Test sets and Cross-validation"
Marsland
- Ch 2, sec. 2.2-2.2.4 (with corrections in the slides)
- Ch 2, sec 2.5 (Not the formulas)
- Ch 13: Introduction, 13.2 Bagging, 13.3 Random forest
Part 1: The context of Multi-layer neural networks
Syllabus (more to be filled in)
Exercise: Work on Mandatory assignment 2
Recommended readings and videos Part 1
- https://www.youtube.com/watch?v=Dk7h22mRYHQ
Interview with Hinton, in particular min. 4-12 - https://en.wikipedia.org/wiki/AI_winter
in particular the first part - If you are interested in reading more about the history of AI, we recommend the recent book by Melanie Mitchell, Artificial Intelligence, 2019
Recommended readings Part 2
Through the UiO library we have now got access to https://www.oreilly.com/library/view/temporary-access/ . Log in with UiO user name. They have many useful books in ML (and computing at large). In particular, they have published some of the best-selling books in ML, including some using scikit-learn. We recommend
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow,
by Aurélien Géron
The following is also useful, cover some of the same, a little less technical:
- Introduction to Machine Learning with Python: A Guide for Data Scientists
by Andreas C. Müller and Sarah Guido