Week Name Description

The results of the first graded session which took place on 2.4.2019. The total number of points is 15.

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

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