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)

Assign3: CSCI4390-6390 Assign3 (Due Date: Oct 9th)

Assign4: CSCI4390-6390 Assign4 (Due Date: Oct 16th)

Assign5: CSCI4390-6390 Assign5 (Due Date: Nov 1st)

Assign6: CSCI4390-6390 Assign6 (Due Date: Nov 9th)

Assign7: CSCI4390-6390 Assign7 (Due Date: Dec 2nd)

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)

PDF6, Video6

Sep 25

EXAM I

Sep 29

Kernel Methods I (Chapter 5)

PDF7, Video7

Oct 02

Kernel PCA & Linear Regression I (Chapters 5, 23)

PDF8, Video8

Oct 06

Linear Regression II (Chapter 23)

PDF9, Video9

Oct 09

Logistic Regression (Chapter 24)

PDF10, Video10

Oct 13

Logistic & Neural Networks (Chapters 24, 25)

PDF11, Video11

Oct 16

Neural Networks I (Chapter 25)

PDF12, Video12

Oct 20

EXAM II

Oct 23

Neural Network II (Chapter 25)

PDF13, Video13

Oct 27

Deep Learning I (Chapter 26)

PDF14, Video14

Oct 30

Deep Learning II (Chap 26) & SVMs I (Chapter 21)

PDF15, Video15

Nov 03

Support Vector Machines II (Chapter 21)

PDF16, Video16

Nov 06

Probabilisitic Classification (Chapter 18)

PDF17, Video17

Nov 10

Classification Assessment (Chapter 22)

PDF18, Video18

Nov 13

EXAM III

Nov 17

Classification Assessment II (Chapter 22)

PDF19, Video19

Nov 20

Representative-based Clustering (Chapter 13)

PDF20, Video20

Nov 24

Density-based Clustering (Chapter 15)

PDF21, Video21

Nov 27

NO CLASS (Thanksgiving)

Dec 01

Spectral and Graph Clustering I (Chapter 16)

PDF22, Video22

Dec 04

Clustering Validation (Chapter 17)

PDF23, Video23

Dec 08

Itemset Mining and Summaries (Chapter 8/9)

PDF24, Video24

Dec 11

EXAM IV