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, Darrin 308
Instructor Office Hours: 12-1PM Mon/Thurs (Lally 209)
TAs (Office Hours):
Aitazaz Khan khana8@rpi.edu (Wed 12-1pm, Thur 1-2pm, AE118)
Syllabus: CSCI4390-6390 Syllabus
Campuswire: https://campuswire.com/c/G35C68FA7
Submitty: https://submitty.cs.rpi.edu/courses/f23/csci4390
Assignments
Assign6: CSCI4390-6390 Assign6, Due: 4th Dec
Assign5: CSCI4390-6390 Assign5, Due: 21th Nov
Assign4: CSCI4390-6390 Assign4, Due: 30th Oct
Assign3: CSCI4390-6390 Assign3, Due: 20th Oct
Assign2: CSCI4390-6390 Assign2, Due: 28th Sep
Assign1: CSCI4390-6390 Assign1, Due: 15th Sep
Class Schedule: Lectures
Tentative course schedule is given below. Lecture notes (in PDF) appear below, and the lecture videos ca n be accessed at the RPI's Mediasite Channel for CSCI4390.
Date | Topic | Lectures |
---|---|---|
Aug 28 | Introduction & Data Matrix (Chapter 1) | lecture1 |
Aug 31 | Data Matrix/Numeric Attributes (Chapters 1 & 2) | lecture2 |
Sep 05 (Tue) | Numeric Attributes (Chapter 2) | lecture3 |
Sep 07 | PCA (Chapter 7) | lecture4 |
Sep 11 | PCA II and Discriminant Analysis (Chapters 7, 20) | lecture5 |
Sep 14 | Discriminant Analysis II, Gradient Descent (Chapter 20) | lecture6 |
Sep 18 | High Dimensional Data I (Chapter 6) | lecture7 |
Sep 21 | High Dimensional Data II, Linear Regression (Chap 6, 7) | lecture8 |
Sep 25 | Linear Regression II (Chapter 23) | lecture9 |
Sep 28 | Linear Regression, Logistic Regression (Chapter 23,24) | lecture10 |
Oct 02 | Exam I | |
Oct 05 | Logistic Regression (Chapter 24) | lecture11 |
Oct 09 | NO CLASS (Columbus Day) | |
Oct 12 | Neural Networks (Chapter 25) | lecture12 |
Oct 16 | Bayes Classifier (Chapter 18) | lecture13 |
Oct 19 | KNN-Classifier, Decision Trees (Chapter 19) | lecture14 |
Oct 23 | Support Vector Machines (Chapter 21) | lecture15 |
Oct 26 | SVMs II, Classification Assessment I (Chapters 21, 22) | lecture16 |
Oct 30 | Classification Assessment II (Chapter 22) | lecture17 |
Nov 02 | EXAM II | |
Nov 06 | Classification Assessment III, Pattern Mining I (Chapters 22, 9) | lecture18 |
Nov 09 | Pattern Mining II (Chapter 9) | lecture19 |
Nov 13 | Representative-Based Clustering I (Chapter 13) | lecture20 |
Nov 16 | Density-based Clustering (Chapter 15) | lecture21 |
Nov 20 | Spectral Clustering (Chapter 16) | lecture22 |
Nov 23 | NO CLASS (Thanksgiving) | |
Nov 27 | Markov Chain Clustering, Hierarchical (Chapters 16, 14) | lecture23 |
Nov 30 | Clustering Validation (Chapters 17) | lecture24 |
Dec 04 | Clustering Validation II (Chapters 17) | lecture25 |
Dec 07 | EXAM III |