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
  [Slides]
  
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  Lecture 2 Python Introduction
  Description: We will go over basics in python
  
  

  Suggested Readings:

  • Python basics (Colab)
  • Numpy (Colab)
  • Scipy (Colab)
  • Matplotlib (Colab)
  • Readings 1
  • Readings 2
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  Lecture 3 Data characteristics and quality
  Description: We will discuss data quality and characteristics with examples
  [Slides]
  
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  Lecture 4 Similarities and distances
  Description: We will discuss multiples measures of similarity and distances between samples
  [Slides]
  
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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
  [Slides]
  
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  Lecture 6 Review
  Description: We will review the homework and material from previous classes
  
  

  Suggested Readings:

  • Correlation (Colab)
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  Lecture 7 Rule Based Classification
  Description: Dr. Verma from Emory University will cover basics of rule based classifiers
  [Slides]
  
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  Lecture 8 Decision Trees I
  Description: This lecture will cover basics of Decision Tree classification model
  [Slides]
  
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  Lecture 9 Decision Trees II
  Description: Here we will continue discussing Decision Trees and see its python implementation
  [Slides]
  

  Suggested Readings:

  • Decision Trees (Colab)
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  Lecture 10 Classifier evaluation and overfitting
  Description: Here we discuss classifier evaluation with focus on accuracy and complexity
  [Slides]
  
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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
  
  

  Suggested Readings:

  • Model selection with generalization error (Colab)
  • Model selection with K-fold CV (Colab)
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  Lecture 17 Imbalanced Classes
  Description: Here we will discuss the concept of imbalanced classes
  
  

  Suggested Readings:

  • Imbalanced Class (Colab)
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  Lecture 18 Support Vector Machines
  Description: Here we will start the discussion of Support Vector Machines algorithm
  [Notes]
  
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  Lecture 19 Support Vector Machines II
  Description: Here we will continue the discussion of SVMs
  [Notes]
  
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  Lecture 20 Support Vector Machines III
  Description: Here we will conclude the discussion of SVMs
  
  

  Suggested Readings:

  • SVMs (Colab)
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  Lecture 22 Ensemble Models I (Bagging)
  Description: We will continue the discussion of ensemble models from previous class
  [Notes]
  

  Suggested Readings:

  • Bagging(Colab)
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  Lecture 23 Ensemble Models II (Boosting)
  Description: We will discuss the details of a Boosting machine learning model
  
  

  Suggested Readings:

  • Boosting (Colab)
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  Lecture 24 K-means clustering
  Description: We will introduce the concept of clustering and discuss K-means clustering
  [Notes]
  

  Suggested Readings:

  • K-means clustering (Colab)