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, Ricketts 203
Instructor Office Hours: 12-1PM Mon/Thurs (Lally 209)
TAs (Office Hours): Qitong Wang wangq19@rpi.edu (Tue & Wed 11AM-12PM, AE 118)
Syllabus: CSCI4390-6390 Syllabus
Submitty: https://submitty.cs.rpi.edu/courses/f25/csci4390
Assignments
Assign5: CSCI4390-6390 Assign5 (Due: Oct 30th, Midnight)
Assign4: CSCI4390-6390 Assign4 (Due: Oct 22nd, Midnight)
Assign3: CSCI4390-6390 Assign3 (Due: Oct 10th, Midnight)
Assign2: CSCI4390-6390 Assign2 (Due: Sep 22nd, Midnight)
Assign1: CSCI4390-6390 Assign1 (Due: Sep 15th, Midnight)
Class Schedule: Lectures
Tentative course schedule is given below. Lecture notes (in PDF) appear below.
| Date | Topic | Lectures |
|---|---|---|
| Aug 28 | Data Matrix/Numeric Attributes (Chapter 1, 2) | lecture1 |
| Sep 04 | Numeric Attributes (Chapter 2) | lecture2 |
| Sep 05 (F) | Numeric Attributes (Chap 2) & High-dim Data (Chap 6) | lecture3 |
| Sep 08 | High-dim Data (Chap 6) | lecture4 |
| Sep 11 | Eigenvectors (Chapter 7) | lecture5 |
| Sep 15 | PCA (Chapter 7) | lecture6 |
| Sep 18 | PCA II (Chapter 7) | lecture7 |
| Sep 22 | Pattern Mining I (Chapter 8) | lecture8 |
| Sep 25 | Pattern Mining II (Chapter 9) | lecture9 |
| Sep 29 | EXAM I | |
| Oct 02 | Pattern Mining & Representative-Based Clustering (Chapters 9, 13) | lecture10 |
| Oct 06 | Representative-Based Clustering II (Chapter 13) | lecture11 |
| Oct 09 | Representative-Based Clustering III (Chapter 13) | lecture12 |
| Oct 13 | NO CLASS (Columbus Day) | |
| Oct 16 | Density-based Clustering (Chapter 15) | lecture13 |
| Oct 20 | Clustering Assessment (Chapter 17) | lecture14 |
| Oct 23 | Guest Lecture (Graph Clustering) | |
| Oct 27 | Bayes Classifier (Chapters 18) | lecture15 |
| Oct 30 | Decision Trees (Chapter 19) | lecture16 |
| Nov 03 | EXAM II | |
| Nov 06 | Support Vector Machines I (Chapter 21) | lecture17 |
| Nov 10 | Support Vector Machines II (Chapter 21) | |
| Nov 13 | Linear Regression (Chapter 23) | |
| Nov 17 | Logistic Regression (Chapter 24) | |
| Nov 20 | Neural Networks I (Chapter 25) | |
| Nov 24 | NO CLASS (Thanksgiving) | |
| Nov 27 | NO CLASS (Thanksgiving) | |
| Dec 01 | Neural Networks II (Chapters 25) | |
| Dec 04 | Classification Assessment (Chapters 22) | |
| Dec 08 | Regression Assessment (Chapter 27) | |
| Dec 11 | EXAM III |