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Summary - Business Administration - Information Management (FiN301)
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Summary of 3 pages for the course BWL - Informationswirtschaft at JWGU (My notes)
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Lecture 9 – Emerging TechnologiesMachines Learning
= Machines will not take over the world, however, ML can improve multiple parts of everyday life
= Using learning algorithms on (big) data to
- Make accurate predictions and detect previously unknown patterns
- Difference to deep learning and AI: Not every system is categorized as an intelligent system uses machine learning
maps an input to an raw data: based on wait for feedback, if
output based on the raw data we want feedback positive
example input-output to learn something algorithm recognizes
pairs about the raw data action as good idea
1. Supervised Learning
Supervised Learning: Regression
Goal: Make quantitative (real valued) predictions on the basis of a (vector of) features or attributes. Reveals causal relationships
between the independent variables (input) and dependent variables (output)
Example with FC Bayern: Features (scored 86% and received goals 78%) have a strong effect on the current position in the table
Supervised Learning: Classification
Goal: Use training data to build a classification model that predicts the
correct category (label) for previously unknown data with high accuracy.
Supervised Learning: Decision Trees
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