You can download the lectures here.

  • Lecture 1 Course Introduction
    Description: We will go over course policies, a brief description and introduction
    [Slides]
  • Lecture 2 Python Introduction
    Description: We will go over basics in python

  • Lecture 3 Data characteristics and quality
    Description: We will discuss data quality and characteristics with examples
    [Slides]
  • Lecture 4 Similarities and distances
    Description: We will discuss multiples measures of similarity and distances between samples
    [Slides]
  • Lecture 5 Data Preprocessing
    Description: We will discuss tools and strategies for preprocessing data to be fed into data mining pipelines
    [Slides]
  • Lecture 6 Review
    Description: We will review the homework and material from previous classes

    Suggested Readings:

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

    Suggested Readings:

  • Lecture 10 Classifier evaluation and overfitting
    Description: Here we discuss classifier evaluation with focus on accuracy and complexity
    [Slides]
  • Lecture 14 Classifier evaluation and KNN
    Description: Here we will discuss classifier evaluation in python with introduction to K-Nearest Neighbors (KNN) algorithm

  • Lecture 17 Imbalanced Classes
    Description: Here we will discuss the concept of imbalanced classes

    Suggested Readings:

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

    Suggested Readings:

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

    Suggested Readings:

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

    Suggested Readings:

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

    Suggested Readings: