Week Name Description
Graded Exercise 2 - Solutions

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

Logistic regression

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

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

Shape from Contours

Human Brains Brain networks
Gerstner: Spiking Neuron Models
Summary Conclusion