Data Science DM
Lecture notes and some additional info from the suggested book
Suggested book: Introduction to Statistical Learning with R
, Data Mining (DM) = the process of discovering patterns in large data sets involving methods at
the intersection of machine learning, statistics, and database systems
1. Supervised techniques
2. Unsupervised techniques
Supervised techniques = learn a target function by examples, learning that involves building a
model for predicting or estimating (an output based on one or more inputs)
Unsupervised techniques = find “obvious” patterns, learning about relationships and structure
of the data
Supervised techniques
1. Decision tree
2. Neural network with weights on connections
3. (Linear) function
Decision tree = decision tree mining
Neural network with weights on connections = deep learning, model
Lecture notes and some additional info from the suggested book
Suggested book: Introduction to Statistical Learning with R
, Data Mining (DM) = the process of discovering patterns in large data sets involving methods at
the intersection of machine learning, statistics, and database systems
1. Supervised techniques
2. Unsupervised techniques
Supervised techniques = learn a target function by examples, learning that involves building a
model for predicting or estimating (an output based on one or more inputs)
Unsupervised techniques = find “obvious” patterns, learning about relationships and structure
of the data
Supervised techniques
1. Decision tree
2. Neural network with weights on connections
3. (Linear) function
Decision tree = decision tree mining
Neural network with weights on connections = deep learning, model
