You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • 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
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  • Lecture 4 Scipy
    Description: We will continue with numpy and discuss basics of scipy
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  • Lecture 5 Matplotlib
    Description: We will discuss basic plotting functionalities provided by matplotlib
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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 9 Linear Regression 1
    Description: We will cover basics of linear regression and implement it in python from scratch
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  • Lecture 10 Linear Regression 2
    Description: We will cover more complex linear regression models
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  • Lecture 11 Ridge Regression
    Description: We will cover basics of Ridge Regression
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  • Lecture 16 Lasso Regression
    Description: We will cover basics of Lasso Regression
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  • Lecture 17 Regression Comparison
    Description: We will compare performance of Linear Regression, Ridge Regression and Lasso Regression
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  • Lecture 18 Classification Introduction
    Description: We will introduce the concept of classification and go over a simple example
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  • Lecture 19 Logistic Regression
    Description: We will discuss the idea behind logistic Regression and apply it on a real problem
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  • Lecture 20 Decision Trees
    Description: We will discuss basics of Decision Tree algorithm for classification
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  • Lecture 21 Random Forest
    Description: We will discuss Random Forest and analyze its classification performance
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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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  • Lecture 23 Classifier Comparison
    Description: We will compare the classification models discussed before
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  • Lecture 24 Classifier Comparison
    Description: We will continue our discussion on comparing classifiers
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  • Lecture 25 K-Nearest Neighbor Classifier
    Description: We will discuss KNN classification algorithm and its application to handwritten digit recognition
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  • Lecture 26 Ensemble models
    Description: We will discuss ensemble models and how they can improve performance of standard data mining approaches
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  • Lecture 27 Ensemble models 2
    Description: We continue the discussion on Ensemble models
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  • Lecture 31 K-Means Clustering
    Description: We introduce clustering in data mining
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