Tools and Techniques for Data Mining and Applications

Boston University CS591 - Spring 2016

This repo holds the materials, lectures and scripts for the Boston University course "Tools and Techniques for Data Mining and Applications". You can find more information about the course here. It is based on the materials developed by Evimaria Terzi and Harry Mavroforakis in Fall 2015 [here] (https://github.com/dataminingapp/dataminingapp-lectures)

Lectures

Lecture 1 - Intro to Python

Lecture 2 - Getting Started| [Pandas] (http://nbviewer.ipython.org/github/datascience16/lectures/blob/master/Lecture2/Getting-to-know-your-data-with-Pandas.ipynb)

Lecture 3 - Distance Functions | Slides

Lecture 4 - Time Series

Lecture 5 - k-means

Lecture 6 - Clustering

Lecture 7 - Hierarchical Clustering

[Lecture 8 - EM] (http://nbviewer.ipython.org/github/datascience16/lectures/blob/master/Lecture8/EM.ipynb)

Lecture 9 - Other clustering algorithms | [Density-slides] (https://github.com/datascience16/lectures/blob/master/Lecture9/density-based-clustering.pdf?raw=true)

Lecture 10 - SVD | Slides

Lecture 11 - SVD in practice | Web scraping slides

Lecture 12 - Classifiaction | [More classification methods] (https://github.com/datascience16/lectures/blob/master/Lecture13/NBSVM.pdf?raw=true) | [SVM] (http://nbviewer.ipython.org/github/datascience16/lectures/blob/master/Lecture14/SVM.ipynb)

Lecture 13 - Classification II | [Slides] (https://github.com/datascience16/lectures/blob/master/Lecture13/kNNHigh.pdf?raw=true) | [RandomP] (http://nbviewer.ipython.org/github/datascience16/lectures/blob/master/Lecture13/RandomP.ipynb)

Lecture 14 - Linear Regression

Lecture 15 - Logistic Regression

Lecture 16 - Linear Regression II

Lecture 17 - Recommender Systems

Lecture 18 - Introduction to graph analysis

Lecture 19 - Node Centralities | [Centrality-slides] (https://github.com/datascience16/lectures/blob/master/Lecture19/Centrality-Measures.pdf?raw=true)

Lecture 20 - Community detection | [Cuts-slides] (https://github.com/datascience16/lectures/blob/master/Lecture20/cuts.pdf?raw=true)

Lecture 21 - Map Reduce

Lecture 22 - Map Reduce Graph Algorithms

Lecture 23 - Computing Triangles slides | [Spark Slides] (https://github.com/datascience16/lectures/blob/master/Lecture23/spark.pdf?raw=true)

Homeworks

The homeworks of this course can be found at this repository.