CSCI 6350 Data Mining

CSCI 6600/7350 - Data Mining
Instructor: Dr. Cen Li
Email: Cen.Li@mtsu.edu
Office Hours: Tue, Wed, Thur, 10:30 am -- 12:00 pm
Other times by appointment.

Tentative Schedule

Week	Date	Topics	Assignment
Week 1	Jan 16th (Tue) and Jan 18th (Thur)	no class
Week 2	Jan 22nd (Tue)	Course Syllabus Intro to Data Mining
Week 2	Jan 24th (Thur)	Intro to Data Mining (continued)	Install: Anaconda.com (Anaconda-Navigator) pip install jupyterlab, jupyter lab
Week 3	Jan 30th	Recommendation System GroupLens web site Notes on Recommendation Program Demo Notes	Project1.pdf (Due: midnight, Thursday, Feb 8th) movies100K.tar.gz In this folder, u.data is the file containing user-movie-ranking information (a larger version of test.dat in class demo) u.item is the file containing movie information (a larger version of movies.dat in class demo) movies.dat test.dat recom.py.txt Program demo result
Week 3	Feb 1st	Association Rule Discovery(Apriori) Example Apryori program(apriori.py) Data file
Week 4	Feb 6th	Association Rule Discovery(Apriori)continued A Complete Example Apyori doc Association Rule Discovery (FP-Growth)
Week 4	Feb 8th	Association Rule Discovery (FP-Growth)(continued) Classification and Regression Intro Linear Regression access simple_regression.ipynb and multiple_linear_regression.ipynb data file (50_Startups.csv) data file (Salary_Data.csv)	Homework 1 (Due midnight Friday, Feb 16th)
Week 5	Feb 13th	Logistic Regression Classification access _regression.ipynb data file	The Derivative of Cost Function for Logistic Regression
Week 5	Feb 15th	K Nearest Neighbor Classification access k_nearest_neighbors.ipynb data file
Week 6	Feb 20th	K Nearest Neighbor Classification(continued)
Week 6	Feb 22nd	Decision Tree Classification(1) In class example notes (This notes show computation for the first 2 attributes. A complete computation should include calculations for all 3 attributes) access decision_tree_classification.ipynb data file draw decision tree	Homework 2
Week 7	Feb 27th	Probability review and Density estimator
Week 7	Feb 29th	Conditional Independence Naive Bayes Classifier access naive_bayes.ipynb	Semester Project
Week 8	March 5th and 7th	Spring break, no class
Week 9	March 12th	Naive Bayes for text classification Artificial Neural Network access artificial_neural_network.ipynb data file
Week 9	March 14th	Ensemble Classifier Boosting notes	Project 2
Week 10	March 19th	Semester Project Proposal In class presentation	Homework 3
Week 10	March 21st	Support Vector Machines and the Kernel Functions
Week 11	March 26th	Feature Selection class notes on univariate feature selection class notes on multivariate feature selection
Week 11	March 28th	Feature Selection(continued)
Week 12	April 2nd	Dimensionality Reduction: PCA and LDA
Week 12	April 4th	Team project work
Week 13	April 9th	Semester project progress report in class presentation
Week 13	April 11th	Partitional Clustering Scikit Learn Clustering Scikit learn KMedoids	Homework 4
Week 14	April 16th	Hierarchical Clustering BIRCH: An Efficient Data Clustering Method for Very Large Databases
Week 14	April 18th	Model based clustering DB Scan:A Density-based Algorithm for Discovering Clusters"
Week 15	April 23rd	Semester project final presentation

Useful Information

OneNote to class notes
Scikit: Machine Learning in Python
Data Mining: Practical Machine Learning Tools and Techniques (Third Edition) by Ian H. Witten, Eibe Frank, Mark A. Hall, Morgan Kaufmann, Jan 2011.
Python Official Web Site
- lamba function tutorial
WEKA home page
KDNuggets
UCI Machine Learning Repository
Sebastian Ruder, "An overview of gradient descent optimization algorithms"
Robi Polikar, "Ensemble Based Systems in Decision Making"