Exam Preparation and HW7 - Learning & Adaptive Systems Group · Introduction to Machine Learning...
Transcript of Exam Preparation and HW7 - Learning & Adaptive Systems Group · Introduction to Machine Learning...
Exam Preparation and HW7Introduction to Machine Learning 2020
Julian Mäder
Schedule
- Exam 2019, Question 1- Exam 2019, Question 2- HW7 Questions 13, 14 and 15
Exam 2019, Question 1
Exam 2019, Question 1.1
Exam 2019, Question 1.1
Exam 2019, Question 1.1
Exam 2019, Question 1.1
Exam 2019, Question 1.2
Exam 2019, Question 1.2
Exam 2019, Question 1.2
First we reformulate the maximum likelihood estimate:
Exam 2019, Question 1.2
Next we look at our assumptions about the data:
Exam 2019, Question 1.2
Exam 2019, Question 1.2So let’s compare the maximum likelihood estimate to the weighted empirical risk:
Exam 2019, Question 1.3
Exam 2019, Question 1.3
...because is not differentiable!
Exam 2019, Question 1.4
Exam 2019, Question 1.5
Recap Kernels
Perceptron:
Recap Kernels
Exam 2019, Question 2.1
⟶ See Kernel Nearest-Neighbor Algorithm, Yu et al. 2002⟶ Lecture Slides: Dimensionality Reduction ��, slides 6 - 13⟶ Lecture Slides: Kernels ��, slides 34 - 37
⟶ Lecture Slides: Dimensionality Reduction ��, slides 6 - 12
Exam 2019, Question 2.2
Exam 2019, Question 2.2
Exam 2019, Question 2.2
Kernel Definition
Kernel Definition
Kernel Rules
Exam 2019, Question 2.3
Exam 2019, Question 2.3
Because c needs to be bigger than Zero!
Exam 2019, Question 2.3
Exam 2019, Question 2.4
Exam 2019, Question 2.4
Exam 2019, Question 2.4
Exam 2019, Question 2.4
Exam 2019, Question 2.5
Exam 2019, Question 2.5
Exam 2019, Question 2.6
Expectation of a (discrete) Random Variable
HW7 Question 13-15: Important Tipps
HW7 Question 13-15: Important Tipps
HW7 Question 13
HW7 Question 13
* Expectation ** Jensen’s Inequality
HW7 Question 14
HW7 Question 14
* Expectation ** Jensen’s Inequality
HW7 Question 14
HW7 Question 15
There is a more detailed explanation in the CS229 lecture notes (Part IX, The EM Algorithm) by Andrew Ng:(https://course.ccs.neu.edu/cs6220f16/sec3/assets/pdf/cs229-notes8.pdf)