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) | lecture24 |
| Dec 06 | Clustering Validation (Chapter 17) | lecture25 |
| Dec 09 | EXAM III |