|
 GRADED EXERCISE 2 |
|
|
 Graded Exercise 2 - Solutions |
|
|
| Grades |
 1st Graded Exercise - Results |
The results of the first graded session which took place on 2.4.2019. The total number of points is 15. |
|
Graded Exercise 1 Solutions |
The solutions of the first graded exercise |
|
2nd Graded Exercise - Results File |
|
|
| Introduction |
Course Introduction |
|
|
Naive Bayes |
|
|
Images as vectors |
|
|
Python and NumPy Primer |
|
|
Exercise Session 1 |
|
|
Solutions |
|
|
| Nearest Neighbors |
K nearest neighbors |
|
|
Bishop: 2.5.2 Nearest-neighbour methods |
|
|
Exercise Session 2 |
|
|
Solutions (jupyter notebook) |
|
|
 Solutions (html) |
|
|
| Linear Classification |
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) |
|
|
| From Linear to Non-Linear Classification |
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) |
|
|
| Neural Nets |
Multi-layer perceptrons |
|
|
Bishop: 5 Neural networks |
|
|
Nielsen: Deep learning tutorial |
|
|
Kingma: Adaptive Moment Estimation |
|
|
Telgarsky: Benefits of depth |
Telgarsky paper showing the benefits of deeper networks. |
|
Convolutional networks |
Convolutional networks designed to handle images. |
|
AlphaGo |
|
|
Exercise Session 7 |
|
|
 solution (notebook) |
|
|
solution (html) |
|
|
Exercise Session 8 |
|
|
Exercise Session 8 - WINDOWS |
|
|
solution (html) |
|
|
Solutions (notebook) |
|
|
| Regression |
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 |
Neural networks and Autoencoders |
|
Exercise Session 9 |
|
|
Exercise 9 Solution (HTML) |
|
|
Exercise 9 Solution |
|
|
Exercise session 10 |
|
|
 Exercise 10 Solution |
|
|
| Modeling over time |
Recurrent networks |
|
|
| Human Brains |
Brain networks |
|
|
Gerstner: Spiking Neuron Models |
|
|
| Summary |
Conclusion |
|
