syllabus
schedule
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Week 1
Introduction and Applications
September 12
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Topics
- A Course Overview
- Data Vocabulary
- Suggested Reading
- Assignment 1 is assigned - Review & Exploring Data
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Topics
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Week 2
Mining for Association Rules
September 19
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Topics
- Definitions of Frequent Itemsets
- Determining Frequent Itemsets
- Creating Association Rules
- Suggested Reading
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Submissions
- Assignment 1 is due
- Assignment 2 is assigned - Association Rules
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Topics
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Week 3
Accessing, Storing, and Computing with "Big" Data
September 26
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Topics
- Distributed Filesystems and Storage
- Introducing the MapReduce Paradigm
- Distributed Computation
- Suggested Reading - Chapter 2, Sections 2.1-2.4
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Submissions
- Assignment 2 is due
- Assignment 3 is assigned - Map Reduce Problem
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Topics
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Week 4
Large Scale Data (Pre)-Processing
October 3
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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
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Topics
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Week 5
Mining Data without Labels
October 10
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Topics
- Introducing the Gaussian Distribution
- Parameter Estimation of a Distribution
- Anomaly and Outlier Detection
- Unsupervised Modeling with k-Means and Clustering
- Suggested Reading
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Submissions
- Assignment 3 is due
- Assignment 4 is assigned - Parameter Estimation & Clustering
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Topics
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Week 6
Mining Small-ish Data - Statistical Learning
October 17
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Topics
- The Bayesian Framework
- Naive Bayes Classification
- Tree-based Algorithms - Random Forests
- Suggested Reading
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Submissions
- Assignment 4 is due
- Assignment 5 is assigned - Bayesian Framework & ML Libraries
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Topics
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Week 7
Midterm Exam
October 24
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Topics
- Linear Algebra Review
- MapReduce Problems
- Principle Component Analysis
- Parameter Estimation
- Unsupervised Clustering
- Bayesian Framework
- Suggested Preparation
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Topics
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Week 8
No Instruction This Week
October 31
- Happy Halloween
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Week 9
Mining Big Data - Foundations of Machine Learning
November 7
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Topics
- Evaluating 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
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Suggested Reading
- Evaluation Metrics, Chapter 8.5
- Logistic Regression ([1], [2])
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Submissions
- Assignment 5 is due
- Assignment 6 is assigned - Evaluation Metrics
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Topics
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Week 10
Mining Images with Deep Learning
November 14
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Topics
- Working with Tensors - Reviewing Multivariate Calculus
- Deep Learning - A Historical Perspective
- The Backpropation Algorithm
- Convolutional Neural Networks
- Suggested Reading
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Submissions
- Assignment 6 is due
- Assignment 7 is assigned - Logistic Regression and Deep Learning
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Topics
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Week 11
Mining Text with Self Supervision
November 21
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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
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Submissions
- Project proposals are recommended
- Assignment 7 is due
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Topics
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Week 12
Data Mining Applications
November 28
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Topics
- Social Network Data Mining
- Recommendation Sciences
- Time Series Analysis
- Suggested Reading
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Topics
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Week 13
Project Presentations and Industry Day
December 5
- Data Mining in Industry
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Submissions
- Final projects are due, including presentation slides and writeup
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Week 14
Final Exam
December 12
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Topics
- Objective Functions
- Logistic Regression
- Association Rule Mining
- Evaluation Metrics
- Backpropagation
- Convolutions and Recurrence
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Topics
grading criterion
| Labs & Participation | 10% |
| Data Mining Project | 10% |
| Assignments | 20% |
| Midterm Exam | 30% |
| Final Exam | 30% |
course meeting times
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Lectures
- Tues, 6pm-9:20pm
- Room TBD
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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