CSCI4390-6390 Data Mining

This course focuses on fundamental algorithms and core concepts in data mining and machine learning. The emphasis is on leveraging geometric, algebraic and probabilistic viewpoints, as well as algorithmic implementation.

Class Hours: 10AM-11:50AM Mon/Thurs, Darrin 308

Instructor Office Hours: 12-1PM Mon/Thurs (Lally 209)

TAs (Office Hours):

Aitazaz Khan (Wed 12-1pm, Thur 1-2pm, AE118)

Syllabus: CSCI4390-6390 Syllabus




Assign6: CSCI4390-6390 Assign6, Due: 4th Dec

Assign5: CSCI4390-6390 Assign5, Due: 21th Nov

Assign4: CSCI4390-6390 Assign4, Due: 30th Oct

Assign3: CSCI4390-6390 Assign3, Due: 20th Oct

Assign2: CSCI4390-6390 Assign2, Due: 28th Sep

Assign1: CSCI4390-6390 Assign1, Due: 15th Sep

Class Schedule: Lectures

Tentative course schedule is given below. Lecture notes (in PDF) appear below, and the lecture videos ca n be accessed at the RPI's Mediasite Channel for CSCI4390.

Date Topic Lectures
Aug 28 Introduction & Data Matrix (Chapter 1) lecture1
Aug 31 Data Matrix/Numeric Attributes (Chapters 1 & 2) lecture2
Sep 05 (Tue) Numeric Attributes (Chapter 2) lecture3
Sep 07 PCA (Chapter 7) lecture4
Sep 11 PCA II and Discriminant Analysis (Chapters 7, 20) lecture5
Sep 14 Discriminant Analysis II, Gradient Descent (Chapter 20) lecture6
Sep 18 High Dimensional Data I (Chapter 6) lecture7
Sep 21 High Dimensional Data II, Linear Regression (Chap 6, 7) lecture8
Sep 25 Linear Regression II (Chapter 23) lecture9
Sep 28 Linear Regression, Logistic Regression (Chapter 23,24) lecture10
Oct 02 Exam I
Oct 05 Logistic Regression (Chapter 24) lecture11
Oct 09 NO CLASS (Columbus Day)
Oct 12 Neural Networks (Chapter 25) lecture12
Oct 16 Bayes Classifier (Chapter 18) lecture13
Oct 19 KNN-Classifier, Decision Trees (Chapter 19) lecture14
Oct 23 Support Vector Machines (Chapter 21) lecture15
Oct 26 SVMs II, Classification Assessment I (Chapters 21, 22) lecture16
Oct 30 Classification Assessment II (Chapter 22) lecture17
Nov 02 EXAM II
Nov 06 Classification Assessment III, Pattern Mining I (Chapters 22, 9) lecture18
Nov 09 Pattern Mining II (Chapter 9) lecture19
Nov 13 Representative-Based Clustering I (Chapter 13) lecture20
Nov 16 Density-based Clustering (Chapter 15) lecture21
Nov 20 Spectral Clustering (Chapter 16) lecture22
Nov 23 NO CLASS (Thanksgiving)
Nov 27 Markov Chain Clustering, Hierarchical (Chapters 16, 14) lecture23
Nov 30 Clustering Validation (Chapters 17) lecture24
Dec 04 Clustering Validation II (Chapters 17) lecture25