Lectures
You can download the lectures here. We will try to upload lectures prior to their corresponding classes.
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Lecture 1 Course Introduction
Description: We will go over course policies, a brief description and introduction
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Lecture 2 Python Introduction
Description: We will go over basic commands in python
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Lecture 3 Numpy
Description: We will discuss basics of numpy library and how it can help with data mining models
[Notebook]
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Lecture 4 Scipy
Description: We will continue with numpy and discuss basics of scipy
[Notebook]
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Lecture 5 Matplotlib
Description: We will discuss basic plotting functionalities provided by matplotlib
[Notebook]
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Lecture 6 Data Characteristics
Description: We will discuss data properties and distance measures
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Lecture 7 Data quality and preprocessing
Description: We will discuss additional aspects of data and various useful preprocessing strategies
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Lecture 8 Introduction to Sklearn
Description: We will get familiar with the sklearn library in python for data mining
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Lecture 21 Random Forest
Description: We will discuss Random Forest and analyze its classification performance
[Notebook]
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Lecture 22 Classifier Evaluation
Description: We will discuss multiple measures of classifier performance for balanced/imbalanced datasets
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