- Iterative process - Non-sequential - Early termination - Established processes, e.g. CRISP-DM
Name the approximately year of invention of Machine Learning, Deep learning and Artificial Intelligence:
AI 1950's Creation of first "intelligent" algorithms and programs
ML 1980's Statistical models and algorithms that can learn from data
DL 2010's Statistical models and algorithms inspired by neurones that can learn from data
Name the 3 main branches of ML and some of its applications:
Customer Retention (Kundenbindung)
Estimating life expextancy
Population Growht Prediction
Big data Visualisation
Explain supervised learning:
In supervised learning the training data consicts of input / output pairs and we train a function to map the inputs to the outputs. The predicted variable consists is therby either a continuous variable like Price / Cost / Weight (Regression Problems) or categorical variable like A, B or C / Dogs or Cats.
Explain unsupervised learning:
In unsupervised learning there are no labels available, insights are gained without prior knowledge.
For Anomaly / Outlier detection is the task, finding samples in a dataset tat raise suspicion. The problem therby is, that you usally do not know, what you are looking for. The solution is to use statistics and characteristics of the dataset to find outliers.