Lecture 9 – Emerging Technologies
Machines 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
Machines 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
