Lectures
You can download the lectures here.
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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 basics in python
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Lecture 3 Data characteristics and quality
Description: We will discuss data quality and characteristics with examples
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Lecture 4 Similarities and distances
Description: We will discuss multiples measures of similarity and distances between samples
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Lecture 5 Data Preprocessing
Description: We will discuss tools and strategies for preprocessing data to be fed into data mining pipelines
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Lecture 6 Review
Description: We will review the homework and material from previous classes
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Lecture 7 Rule Based Classification
Description: Dr. Verma from Emory University will cover basics of rule based classifiers
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Lecture 8 Decision Trees I
Description: This lecture will cover basics of Decision Tree classification model
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Lecture 9 Decision Trees II
Description: Here we will continue discussing Decision Trees and see its python implementation
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Lecture 10 Classifier evaluation and overfitting
Description: Here we discuss classifier evaluation with focus on accuracy and complexity
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Lecture 14 Classifier evaluation and KNN
Description: Here we will discuss classifier evaluation in python with introduction to K-Nearest Neighbors (KNN) algorithm
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Lecture 17 Imbalanced Classes
Description: Here we will discuss the concept of imbalanced classes
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Lecture 18 Support Vector Machines
Description: Here we will start the discussion of Support Vector Machines algorithm
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Lecture 19 Support Vector Machines II
Description: Here we will continue the discussion of SVMs
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Lecture 20 Support Vector Machines III
Description: Here we will conclude the discussion of SVMs
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Lecture 22 Ensemble Models I (Bagging)
Description: We will continue the discussion of ensemble models from previous class
[Notes]
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Lecture 23 Ensemble Models II (Boosting)
Description: We will discuss the details of a Boosting machine learning model
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Lecture 24 K-means clustering
Description: We will introduce the concept of clustering and discuss K-means clustering
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