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GRADED EXERCISE 2
GRADED EXERCISE #2 SUBMISSION LINK
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Graded Exercise 2 - Solutions
1st Graded Exercise - Results
Graded Exercise 1 Solutions
2nd Graded Exercise - Results File
Course Introduction
Naive Bayes
Images as vectors
Python and NumPy Primer
Exercise Session 1
Solutions
K nearest neighbors
Bishop: 2.5.2 Nearest-neighbour methods
Exercise Session 2
Solutions (jupyter notebook)
Solutions (html)
Logistic regression
Bishop: 4.1.7 The Perceptron Algorithm
Bishop: 4.3.2 Logistic regression
Optimization
Triggs: Bundle Adjustment — A Modern Synthesis
Exercise Session 3
Solutions (jupyter notebook)
Solutions (html)
Exercise Session 4
Solutions (jupyter notebook)
Solutions (html)
High-Dimensional Features and Kernels
Proof of Cover's theorem
Bishop: 7.1 Maximum margin classifiers
Support Vector Machine vs Logistic Regression
Boosting
Bishop: 14.3 Boosting
Trees and Forests
Bishop: 14.4 Tree-based models
Exercise Session 5
Solution(jupyter notebook)
Solution(html)
Exercise Session 6
Solution(notebook)
Solution(html)
Multi-layer perceptrons
Bishop: 5 Neural networks
Nielsen: Deep learning tutorial
Kingma: Adaptive Moment Estimation
Telgarsky: Benefits of depth
Convolutional networks
AlphaGo
Exercise Session 7
solution (notebook)
solution (html)
Exercise Session 8
Exercise Session 8 - WINDOWS
solution (html)
Solutions (notebook)
Linear regression and least squares
Bishop: 3.1 Linear basis function models
Non-linear least squares
Fua: Constrained least squares
Gaussian processes
Bishop: 6.4 Gaussian processes
Regression Trees
Caruana: An empirical evaluation of supervised learning in high dimensions
Neural Nets
Exercise Session 9
Exercise 9 Solution (HTML)
Exercise 9 Solution
Exercise session 10
Exercise 10 Solution
Brain networks
Gerstner: Spiking Neuron Models
Conclusion
Brain networks ►