syllabus | Data Mining CS 6220

schedule

Week 1
Introduction and Applications

January 11
- Topics
  - A Course Overview
  - Data Vocabulary
- Suggested Reading
- Assignment 1 is assigned - Review & Exploring Data
Week 2
Mining for Association Rules

January 18
- Topics
  - Definitions of Frequent Itemsets
  - Determining Frequent Itemsets
  - Creating Association Rules
  - Working with Docker
- Suggested Reading
- Submissions
  - Assignment 1 is due
  - Assignment 2 is assigned - Association Rules
Week 3
Accessing, Storing, and Computing with "Big" Data

January 25
- Topics
  - Distributed Filesystems and Storage
  - Structured Data Analysis (SQL)
  - Introducing the MapReduce Paradigm
  - Distributed Computation
- Suggested Reading - Chapter 2, Sections 2.1-2.4
- Submissions
  - Recitation video is available
  - Assignment 2 is due
  - Assignment 3 is assigned - Map Reduce Problem
Week 4
Large Scale Data (Pre)-Processing

February 1
- Topics
  - Basics of Linear Algebra and Probability Theory
  - The Multiple Places Where Data Lives & Multi-source Joins
  - Covariance, Correlation, and Cosine Similarity
  - Dimensionality Reduction and Feature Selection
- Suggested Reading
Week 5
Mining Data without Labels

February 8
- Topics
  - Introducing the Gaussian Distribution
  - Parameter Estimation of a Distribution
  - Unsupervised Modeling with k-Means and Clustering
- Suggested Reading
  - Maximum Likelihood
  - Unsupervised Clustering, Chapter 7 - 7.2
- Submissions
  - Assignment 3 is due
  - Assignment 4 is assigned - Parameter Estimation & Clustering
Week 6
Mining with Bayes Classifiers

February 15
- Topics
  - Anomaly and Outlier Detection
  - The Bayesian Framework
  - Naive Bayes Classification
- Suggested Reading
  - Bayes Theorem
  - Naïve Bayes
- Submissions
  - Assignment 4 is due
  - Assignment 5 is assigned - Bayesian Framework & ML Libraries
Week 7
Mining with Small Data and Course Review

February 22
- Topics
  - Decision Tree Classification
  - Course Review and Midterm Preparation
- Suggested Reading
  - Tree Algorithms - Chapters 3.1 - 3.3
- Submissions
  - Assignment 5 is due
  - Assignment 6 is assigned - Evaluation Metrics
Week 8
Midterm Exam

February 29
- Topics
  - Linear Algebra Review
  - MapReduce Problems
  - Principle Component Analysis
  - Parameter Estimation
  - Unsupervised Clustering
  - Bayesian Framework
- Suggested Preparation
Week 9
No Instruction This Week - Spring Break

March 7
- Have a nice break!
Week 10
No Instruction This Week - Instructor Absence

March 14
- Office hours held instead
Week 11
Foundations of Machine Learning

March 21
- Topics
  - Algorithmic Evaluation with Confusion Matrices, Thresholds, ROC Curves
  - The Objective Function, Regularization, and Constraints
  - Logistic Regression - Precursor to Modern Data Mining
  - Batch Data Processing - Gradient Descent
  - The Bias and Variance Tradeoff
- In-Class Colabs: Logistic Regression with MNIST
- Suggested Reading
  - Evaluation Metrics, Chapter 8.5
  - Logistic Regression ([1], [2])
- Submissions
  - Project Proposals are Due - Initial Thoughts and Feedback
Week 12
Mining Images with Deep Learning

March 28
- Topics
  - Working with Tensors - Reviewing Multivariate Calculus
  - Deep Learning - A Historical Perspective
  - The Backpropation Algorithm
  - Convolutional Neural Networks
- Suggested Reading
- Submissions
  - Assignment 6 is due
  - Assignment 7 is assigned
Week 13
Mining Text with Self Supervision

April 4
- Topics
  - Some Basic Approaches
  - Semi-Supervised Learning
  - The Concept of an Embedding Space
  - The Attention Mechanism
  - Large Language Models - From BERT to ChatGPT
- Suggested Reading
  - Information Retrieval, Chapter 13
Week 14
Industry Day

April 11
- Data Mining in Industry and in Practice
- Submissions
  - Assignment 7 is due
Week 15
Project Presentations and Final Review

April 18
- Project Presentations and Outbriefs
  - Please add your presentations to this slide deck.
  - Feel free to use templates for slides and writeup
- Submissions
  - Final projects are due, including presentation slides and writeup
Week 16
Final Exam

April 25
- Topics
  - Objective Functions
  - Logistic Regression
  - Association Rule Mining
  - Evaluation Metrics
  - Backpropagation
  - Convolutions and Recurrence

grading criterion

Labs & Participation	10%
Data Mining Project	10%
Assignments	20%
Midterm Exam	30%
Final Exam	30%

course meeting times

Lectures
- Tues, 6pm-9:20pm
- Room TBD
Office Hours
- Professor, Thurs, 8:30-9:30pm
- TA, Date/Time TBD

suggested textbooks

Introduction to Data Mining, 2nd Edition Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, 2018
Mining of Massive Data Sets, 3rd Edition Jure Leskovec, Anand Rajaraman, and Jeff Ullman, 2014
Deep Learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016

schedule

January 11

January 18

January 25

February 1

February 8

February 15

February 22

Midterm Exam

February 29

No Instruction This Week - Spring Break

March 7

No Instruction This Week - Instructor Absence

March 14

March 21

March 28

April 4

April 11

April 18

Final Exam

April 25

grading criterion

course meeting times

Lectures

Office Hours

suggested textbooks