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: Troy 2012, 10AM-11:50AM Mon/Thurs

Instructor Office Hours: 12-1PM Mon/Thurs (MRC335)

TAs (Office Hours): Anweshit Panda (pandaa2@rpi.edu); Hours 2-3PM Tue/Wed (AE111)

Syllabus: CSCI4390-6390 Syllabus

Submitty: https://submitty.cs.rpi.edu/courses/f26/csci4390

Assignments

Assign1: CSCI4390-6390 Assign1 (Due: Sep 14th, Midnight)

Class Schedule: Lectures

Tentative course schedule is given below. Lecture notes (in PDF) appear below.

Date Topic Lectures
Aug 27 Data Matrix/Numeric Attributes (Chapters 1&2) lecture1
Aug 31 Numeric Attributes (Chapter 2) lecture2
Sep 03 Numeric Attributes II (Chapter 2) lecture3
Sep 07 NO CLASS (Labor Day)
Sep 10 PCA (Chapter 7) lecture4
Sep 14 PCA II and LDA I (Chapters 7 and 20) lecture5
Sep 17 LDA II (Chap 20)
Sep 21 Linear Regression I (Chapter 23)
Sep 24 Linear Regression II (Chapter 23)
Sep 28 EXAM I
Oct 01 Pattern Mining I (Chapter 8)
Oct 05 Pattern Mining II (Chapter 9)
Oct 08 Representative-Based Clustering I (Chapter 13)
Oct 12 NO CLASS (Columbus Day)
Oct 16(F) Representative-Based Clustering II (Chapter 13)
Oct 19 Density-based Clustering (Chapter 15)
Oct 22 Bayes Classifier (Chapters 18)
Oct 26 Decision Trees (Chapter 19)
Oct 29 Support Vector Machines I (Chapter 21)
Nov 02 EXAM II
Nov 05 Support Vector Machines II (Chapter 21)
Nov 09 Logistic Regression (Chapter 24)
Nov 12 Neural Networks I (Chapter 25)
Nov 16 Neural Networks II (Chapter 25)
Nov 19 Modern Hopfield Networks I
Nov 23 NO CLASS (Thanksgiving)
Nov 26 NO CLASS (Thanksgiving)
Nov 30 Modern Hopfield Networks II
Dec 03 Assessment I (Chapters 17&22)
Dec 07 Assessment II (Chapters 17&22)
Dec 10 EXAM III