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Recommender Systems III
General Where we focus User-Item RecommendationItem-Item RecommendationContent based recommendationCollaborative Filtering Recall: Notes on Item-Item Recommendations recommendation based on last item:“If you are interested in this, you probably also like …”doesn’t…
Feature Extraction and Deep Learning
Feature Extraction Recall Supervised Classification the chosen vector space representation aligns the chosen model and how we make a classificationearlier, we took pixel by pixel and write it in the…
SVMs, Model Selection und Outlier Detection
Non-linear Models II Support Vector Machines Summary: work with small data setsfor classification and regressiongives us the “one” best solutionnon-linear → many possibilitiescan be computing intensive with many classes, large…
Linear and Non-Linear ML Models
Linear Models Recall Classification: → there are other possibilities than Gaussian, e.g. geometrically Parameterization e.g. straight line (linear, 2D) How to find the Parameters? → we “draw” a line between…
Machine Learning
Introduction to Machine Learning We are here: Basic Definitions an Terminology: Basic Types of ML Algorithms: Supervised LearningLabeled dataDirect and quantitative evaluationmore present, but has limitsUnsupervised LearningLearn models from “ground…
Basic Statistics and Data Wrangling
Revision of Basic Statistics Types of Data Continuous Data→ Data that can take any value in an intervall (also float, numeric, interval data)Discrete Data→ Data that can take only integer…
Recommender Systems
Intro Recommender Systems Definition: make product/service recommendations to people. Recommender systems want to identify items that are more relevant, so people consume more omnipresent in every big online store, streaming platformAmazon Example:…