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CSCI4390/6390: Data Mining, Fall 2015
Class Time: TF 1011:50AM Room: Low 3051 Instructor Office Hours: TF 121PM, Lally 307
TA: Niu Xiang TA Office Hours: MR 45PM, AE 119 TA Contact:
,
Announcements
 Nov 25, Assign5? has been posted online with due date: 7th Dec, 2015.
 Nov 16, Assign4? has been posted online with due date: 23rd Nov, 2015.
 Oct 22, Assign3? has been posted online with due date: 30th Oct, 2015.
 Sep 29, Assign2? has been posted online with due date: 6th Oct, 2015.
 Sep 13, Assign1? has been posted online with due date: 21st Sep, 2015.
 Sep 7: Email invitations for Piazza were sent. If you did not get it, please email me.
 Aug 6: Course website is up, with the syllabus and tentative calendar. We will use the Piazza site for discussions and Q&A; an invitation to signup on Piazza will be sent later.

Calendar & Lecture Notes
A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.
Day: Date
 Topic
 Readings
 Lectures

T: Sep 1
 Data Mining and Analysis: Intro
 Chapter 1
 Attach:intro.pptx

F: Sep 4
 Algebraic and Probabilistic Views
 Chapter 1
 Attach:slideschap1.pdf

T: Sep 8
 Numeric Attributes & Eigendecomposition
 Chapter 2
 Attach:slideschap2.pdf

F: Sep 11
 Eigendecomposition
 Chapters 2, 3
 Attach:slideschap2.pdf

T: Sep 15
 Categorical Data, High dimensional Data
 Chapters 3, 6
 Attach:slideschap3.pdf, Attach:slideschap6.pdf

F: Sep 18
 Dimensionality Reduction, Classification: Linear Discriminants
 Chapters 7, 20
 Attach:slideschap7.pdf, Attach:slideschap20.pdf

T: Sep 22
 LDA, SVD, Kernels
 Chapters 20, 7, 5
 Attach:slideschap7.pdf, Attach:slideschap20.pdf

F: Sep 25
 kernels, SVM
 Chapters 5, 21
 Attach:slideschap5.pdf, Attach:slideschap21.pdf

T: Sep 29
 SVMs
 Chapter 21
 Attach:slideschap21.pdf

F: Oct 2
 Prof. Jiawei Han Lecture: CBIS Auditorium (9:45am11am)

T: Oct 6
 SVMs, kernel PCA, Kernel LDA
 Chapters 20, 21, 7
 Attach:slideschap21.pdf, Attach:slideschap20.pdf, Attach:slideschap7.pdf

F: Oct 9
 EXAM I

T: Oct 13
 NO CLASS (Mon Schedule)

F: Oct 16
 Bayes Classifier, Decision Trees
 Chapters 18, 19
 Attach:slideschap18.pdf, Attach:slideschap19.pdf

T: Oct 20
 Neural Networks
 Readings: NNchapter2M.pdf
 Attach:slidesNN.pdf

F: Oct 23
 Classification Evaluation
 Chapter 22
 Attach:slideschap22.pdf

T: Oct 27
 Regression
 Readings: regression.pdf
 Attach:slidesregression.pdf

F: Oct 30
 NO CLASS

T: Nov 3
 KMeans/EM Clustering
 Chapter 13
 Attach:slideschap13.pdf

F: Nov 6
 Hierarchical & Densitybased Clustering
 Chapter 14, 15
 Attach:slideschap14.pdf, Attach:slideschap15.pdf

T: Nov 10
 EXAM II

F: Nov 13
 Spectral & Graph Clustering
 Chapter 16
 Attach:slideschap16.pdf

T: Nov 17
 Cluster Evaluation
 Chapter 17
 Attach:slideschap17.pdf

F: Nov 20
 Frequent Pattern Mining: Itemset Mining
 Chapters 8,9
 Attach:slideschap8.pdf, Attach:slideschap9.pdf

T: Nov 24
 Graph Mining
 Chapter 11
 Attach:slideschap11.pdf

F: Nov 27
 NO CLASS (Thanksgiving Break)

T: Dec 1
 Pattern Assessment
 Chapter 12
 Attach:slideschap12.pdf

F: Dec 4
 Graph Analysis
 Chapter 4

T: Dec 8
 Graph Analysis & Wrapup
 Chapter 4
 Attach:slideschap4.pdf

F: Dec 11
 EXAM III

Syllabus
Introduction
Data mining is the process of automatic discovery of patterns, models, and anomalies in massive databases. This course will provide an introduction to the main topics in data mining and knowledge discovery, including: algebraic and statistical foundations, pattern mining, classification, regression, and clustering. Emphasis will be laid on the algorithmic approach.
Learning Objectives
After taking this course students will be
 able to describe the fundamental data mining tasks like pattern mining, classification, regression and clustering
 able to analyze the key algorithms for the main tasks
 able to implement and apply the techniques to real world datasets
Prerequisites
The prerequisites for this course include data structures and algorithms and discrete mathematics. Linear algebra and probability & statistics are also prerequisites, though an attempt will be made to review the basic concepts. Assignments will require the use of the python language, with NumPy package for numeric computations.
You are expected to learn python on your own via web tutorials, etc.
Textbook
The main required textbook for the course is:
Readings from the will be posted on the course schedule, and supplementary material will be provided where necessary.
Grading Policy
Your grade will be a combination of the following items.
 Exams (50%): There will be three exams covering the main topics of the course. The tentative exam schedule is posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
 Assignments & HW (30%): The assignments are meant to be practically oriented, thought they may include other questions. You'll be asked to implement some algorithms and apply them to real datasets, to complement the theory. There will be roughly one assignment every two weeks (56 assignments in total).
 Data Mining Challenge Problem (20%): We will endeavor to take part in a public data mining competition, e.g., those held by Kaggle. Details will be provided later. This will involve applying data mining methods to realworld challenge tasks, and then assessing the results in a blind evaluation. If this is not feasible due to any reason, the 20% will be distributed equally among the other two categories (e.g., exams 60%, assignments: 40%).
Other Policies
 Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes. Students are responsible for any topics and assignments for the missed classes.
 Laptops: Absolutely no laptops will be allowed in class during lectures or exams.
 Late Assignments: Most assignments will be due just before midnight on the due date. Students can get an automatic one day extension for a 20% grade penalty. No late assignments will be accepted after the midnight following the due date.
Academic Integrity
You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source.
The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the Rensselaer Handbook of Student Rights and Responsibilities on this topic.

