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.

This class will be online for the entire semester, and live class videos will be using webex link below.

Webex Lectures: https://rensselaer.webex.com/meet/zakim

Class Hours: 10:10AM-12:00PM Tue/Fri Online via Webex

Instructor Office Hours: 12-1PM Tue/Fri

TA: Abhishek Gupta guptaa10@rpi.edu

TA Office Hours: 3-4PM Wed and 4-5PM on Thurs via Webex (https://rensselaer.webex.com/meet/guptaa10)

Syllabus: CSCI4390-6390 Syllabus

Campuswire: https://campuswire.com/c/GC1A29D57/

Submitty: https://submitty.cs.rpi.edu/courses/f20/csci4390

Assignments

Assign1: CSCI4390-6390 Assign1 (Due Date: Sep 11th)

Assign2: CSCI4390-6390 Assign2 (Due Date: Sep 22nd)

Class Schedule: Lectures

Tentative course schedule is given below. The topics are subject to change, but the dates for the Exams are fixed.

Date

Topic

Lecture Notes & Videos

Sep 01

Introduction & Data Matrix (Chapter 1)

PDF1, Video1

Sep 04

Data Matrix/Numeric Attributes (Chapter 2)

PDF2, Video2

Sep 08

NO CLASS (Monday Schedule)

Sep 11

Numeric Attributes (Chapter 2)

PDF3, Video3

Sep 15

Dimensionality Reduction (Chapter 7)

PDF4, Video4

Sep 18

High Dimensional Data (Chapter 6)

PDF5, Video5

Sep 22

High Dimensional Data II (Chap 6) & Kernel Methods (Chapter 5)

PDF6, Video6

Sep 25

EXAM I

Sep 29

Kernel Methods II (Chapter 5)

Oct 02

Linear Regression I (Chapter 23)

Oct 06

Linear Regression II (Chapter 23)

Oct 09

Logistic Regression (Chapter 24)

Oct 13

Neural Networks I (Chapter 25)

Oct 16

Neural Networks II (Chapter 25)

Oct 20

EXAM II

Oct 23

Deep Learning I (Chapter 26)

Oct 27

Deep Learning II (Chapter 26)

Oct 30

Support Vector Machines I (Chapter 21)

Nov 03

Support Vector Machines II (Chapter 21)

Nov 06

Probabilisitic Classification (Chapter 18)

Nov 10

Classification Assessment (Chapter 22)

Nov 13

EXAM III

Nov 17

Representative-based Clustering (Chapter 13)

Nov 20

Representative-based Clustering II (Chapter 13)

Nov 24

Density-based Clustering (Chapter 15)

Nov 27

NO CLASS (Thanksgiving)

Dec 01

Spectral and Graph Clustering I (Chapter 16)

Dec 04

Spectral and Graph Clustering II (Chapter 16)

Dec 08

Itemset Mining (Chapter 8)

Dec 11

EXAM IV