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, West Hall Auditorium (2nd Floor)

Instructor Office Hours: 12-1PM Mon/Thurs

TA: Qitong Wang wangq19@rpi.edu, Ian Bogle boglei@rpi.edu

TA Office Hours (online): Ian Bogle (Mon 2-3pm; webex), Qitong Wang (Wed 2-3pm & Fri 2-3pm; webex).

Syllabus: CSCI4390-6390 Syllabus

Campuswire: https://campuswire.com/c/G4CE9F922/

Submitty: https://submitty.cs.rpi.edu/courses/f21/csci4390

Assignments

HW8: CSCI4390-6390 Assign8, Due: 6th Dec

HW7: CSCI4390-6390 Assign7, Due: 22nd Nov

HW6: CSCI4390-6390 Assign6, Due: 15th Nov

HW5: CSCI4390-6390 Assign5, Due: 29nd Oct

HW4: CSCI4390-6390 Assign4, Due: 22nd Oct

HW3: CSCI4390-6390 Assign3, Due: 8th Oct

HW2: CSCI4390-6390 Assign2, Due: 23rd Sep

HW1: CSCI4390-6390 Assign1, Due: 16th Sep

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 Lectures
Aug 30 Introduction & Data Matrix (Chapter 1) lecture1
Sep 02 Data Matrix/Numeric Attributes (Chapter 2) lecture2
Sep 07 (Tue) Numeric Attributes (Chapter 2) lecture3
Sep 09 Dimensionality Reduction I (Chapter 7) lecture4
Sep 13 Dimensionality Reduction II (Chapter 7), High Dimensional Data (Chapter 6) lecture5
Sep 16 High Dimensional Data (Chapter 6) lecture6
Sep 20 Kernel Methods I (Chap 5) lecture7
Sep 23 Kernel PCA & Linear Discriminants (Chapter 7, 20) lecture8
Sep 27 Linear Discriminants II (Chapters 20) lecture9
Sep 30 EXAM I
Oct 04 Linear Regression (Chapter 23) lecture10
Oct 07 Linear Regression II (Chapters 23) lecture11
Oct 11 NO CLASS (Columbus Day)
Oct 14 Regularization and Kernel Regression (Chapter 23), Support Vector Machines (Chapter 21) lecture12
Oct 18 Support Vector Machines II (Chapter 21) lecture13
Oct 21 Logistic Regression (Chapter 24) lecture14
Oct 25 Neural Networks (Chapter 25) lecture15
Oct 28 Neural Networks II (Chap 25) lecture16
Nov 01 Deep Learning (Chapter 26) lecture17
Nov 04 EXAM II
Nov 08 Probabilisitic Classification (Chapter 18) lecture18
Nov 11 Representative-based Clustering (Chapter 13) lecture19
Nov 15 EM-Clustering (Chapter 13); Density-based Clustering (Chapter 15) lecture20
Nov 18 Density-based Clustering II (Chapter 15) lecture21
Nov 22 Spectral Clustering (Chapter 16) lecture22
Nov 25 NO CLASS (Thanksgiving)
Nov 29 Classification Assessment (Chapter 22) lecture23
Dec 02 Classification Assessment II (Chapter 22)
Dec 06 Clustering Validation (Chapter 17)
Dec 09 EXAM III