Week Name Description
File Graded Exercise 2 - Solutions
Grades File 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.

File Graded Exercise 1 Solutions

The solutions of the first graded exercise

File 2nd Graded Exercise - Results File
Introduction File Course Introduction
File Naive Bayes
File Images as vectors
File Python and NumPy Primer
File Exercise Session 1
File Solutions
Nearest Neighbors File K nearest neighbors
URL Bishop: 2.5.2 Nearest-neighbour methods
File Exercise Session 2
File Solutions (jupyter notebook)
File Solutions (html)
Linear Classification File Logistic regression
URL Bishop: 4.1.7 The Perceptron Algorithm
URL Bishop: 4.3.2 Logistic regression
File Optimization
URL Triggs: Bundle Adjustment — A Modern Synthesis
File Exercise Session 3
File Solutions (jupyter notebook)
File Solutions (html)
File Exercise Session 4

Logistic regression

File Solutions (jupyter notebook)
File Solutions (html)
From Linear to Non-Linear Classification File High-Dimensional Features and Kernels
URL Proof of Cover's theorem
URL Bishop: 7.1 Maximum margin classifiers
URL Support Vector Machine vs Logistic Regression
File Boosting
URL Bishop: 14.3 Boosting
File Trees and Forests
URL Bishop: 14.4 Tree-based models
File Exercise Session 5
File Solution(jupyter notebook)
File Solution(html)
File Exercise Session 6
File Solution(notebook)
File Solution(html)
Neural Nets File Multi-layer perceptrons
URL Bishop: 5 Neural networks
URL Nielsen: Deep learning tutorial

Deep Learning Tutorial

URL Kingma: Adaptive Moment Estimation
URL Telgarsky: Benefits of depth

Telgarsky paper showing the benefits of deeper networks. 

File Convolutional networks

Convolutional networks designed to handle images. 

File AlphaGo
File Exercise Session 7
File solution (notebook)
File solution (html)
File Exercise Session 8
File Exercise Session 8 - WINDOWS
File solution (html)
File Solutions (notebook)
Regression File Linear regression and least squares
URL Bishop: 3.1 Linear basis function models
File Non-linear least squares
URL Fua: Constrained least squares
File Gaussian processes
URL Bishop: 6.4 Gaussian processes
File Regression Trees
URL Caruana: An empirical evaluation of supervised learning in high dimensions
File Neural Nets

Neural networks and Autoencoders

File Exercise Session 9
File Exercise 9 Solution (HTML)
File Exercise 9 Solution
File Exercise session 10
Folder Exercise 10 Solution
Modeling over time File Recurrent networks

Shape from Contours

Human Brains File Brain networks
URL Gerstner: Spiking Neuron Models
Summary File Conclusion