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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:
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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, Classification Evaluation  
T: Oct 20  Logistic Regression  
F: Oct 23  Clustering: Partitional & EM  
T: Oct 27  Hierarchical, Densitybased Clustering  
F: Oct 30  Spectral & Graph Clustering  
T: Nov 3  Spectral & Graph Clustering, Evaluation  
F: Nov 6  Cluster Evaluation & Assessment  
T: Nov 10  EXAM II  
F: Nov 13  Frequent Pattern Mining: Itemset Mining  
T: Nov 17  Pattern Summarization  
F: Nov 20  Sequence Mining  
T: Nov 24  Graph Mining  
F: Nov 27  NO CLASS (Thanksgiving Break)  
T: Dec 1  Pattern Assessment  
F: Dec 4  Graph Analysis  
T: Dec 8  Kernels & Graphs  
F: Dec 11  EXAM III 
Syllabus
IntroductionData 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 ObjectivesAfter taking this course students will be
PrerequisitesThe 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. TextbookThe 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 PolicyYour grade will be a combination of the following items.
Other Policies
Academic IntegrityYou 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. 