Overview. Syllabus. How to succeed.
How do we view data? How do we analyze data? What are some basic properties of the data?
What items or events tend to happen together?
How do we use user ratings to recommend something ?
How do we find natural groupings in the data?
How do we handle large data sets?
Probabilistic Models
Decision Tree Models
The curse of high dimensionality
Linear combination of features.
Support Vector Machines
Linear regression for categorical probabilities.
What features to use for what data?
From neural networks to deep learning.
The dark side of big data analytics.
Apply all the concepts you’ve learned in a realistic scenario.