Duration: from 5 hours (incl. lunch break)
Contents: Data Science, scalar product, vectors, matrices, Machine Learning
Previous knowledge: Function concept, differential calculus, linear systems of equations
Participants: Mathematics courses from grade 10
Created by: Sarah Schönbrodt
Registration: Appointments can be made individually using this form.
Netflix, Amazon, Zalando and others are focusing on one thing in particular when it comes to customer loyalty: good recommendations for new products, films, clothing, etc. that are tailored to the user. To this end, these companies develop recommendation systems that can predict as well as possible what the respective user might like.
To further improve its own recommendation system, Netflix set up a challenge in 2006: The team that could predict at least 10% more accurately which movies a user would like had a chance to win the grand prize of $1 million!
In the workshop you will work with the original data from the Netflix Challenge and develop your own recommendation system. You will apply common strategies from the field of artificial intelligence.
Getting started with recommender systems and the Netflix Challenge
Introduction and overview of the Netflix data set.
Understanding data, mean, standard deviation.
30 - 40
Discuss findings from analysis of data set
Recognize correlations in valuation data, work out idea of factorization
Understanding of data, vectors, scalar product
matrices, matrix multiplication
Discussion of captured relationships in the data, backup of the factorization idea.
Determination of a decomposition by hand, evaluation of the quality of a found decomposition (factorization)
Sum of the error squares
30 - 40
Development of an optimization procedure to determine a factorization
derivatives, minimization problem, linear system of equations
Functions in two variables, gradient
50 - 60
Application of the optimization procedure from AB 4 (long version
Application to Netflix dataset
Understanding of training and test data
Interpretation and critical reflection of the results
Principle of machine learning
20 - 25
Comparison of the obtained results on the Netflix dataset.
Social discussion of the use of recommender systems.
Additional sheet: Scalar product (linked to AB2)
Mathematical reasoning for larger or smaller predicted valuations
Scalar product, cosine, vectors, angles
Additional sheet: error measures
Comparison of different error functions
Functions / function graphs
Optimization: Properties of the objective function
Additional sheet: Regularization (linked to AB4)
Regularization - avoiding overfitting to the training data
Graphs of functions
Optimization: existence and uniqueness of a solution
Additional sheet: own recommendations (linked to AB 5)
Give own evaluations and calculate individual recommendations