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Morten Hjorth-Jensen
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doc/LectureNotes/_build/jupyter_execute/week35.ipynb
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#
# 2. [Video of lecture](https://youtu.be/VKakN-e4aUA)
#
# 3. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
# 3. [Video for exercises week 35](https://youtu.be/yiY0OltU1s8)
#
# 4. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
# 4. Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
#
# 5. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
# 5. Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
#
# 6. For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
# ## For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week
#