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, Troy 2012
Instructor Office Hours: 12-1PM Mon/Thurs (Lally 209)
TAs (Office Hours): Qitong Wang wangq19@rpi.edu (Tue 4-5pm, Wed 11-12pm, AE 118)
Syllabus: CSCI4390-6390 Syllabus
Submitty: https://submitty.cs.rpi.edu/courses/f24/csci4390
Assignments
Assign8: CSCI4390-6390 Assign8, Due: 25th Nov
Assign7: CSCI4390-6390 Assign7, Due: 18th Nov
Assign6: CSCI4390-6390 Assign6, Due: 8th Nov
Assign5: CSCI4390-6390 Assign5, Due: 31st Oct
Assign4: CSCI4390-6390 Assign4, Due: 14th Oct
Assign3: CSCI4390-6390 Assign3, Due: 7th Oct
Assign2: CSCI4390-6390 Assign2, Due: 27th Sep
Assign1: CSCI4390-6390 Assign1, Due: 17th Sep
Class Schedule: Lectures
Tentative course schedule is given below. Lecture notes (in PDF) appear below.
Date | Topic | Lectures |
---|---|---|
Aug 29 | NO CLASS | |
Sep 03 (Tue) | Data Matrix/Numeric Attributes (Chapter 1, 2) | lecture1 |
Sep 05 | Numeric Attributes (Chapter 2) | lecture2 |
Sep 09 | Eigenvectors | lecture3 |
Sep 12 | PCA (Chapter 7) | lecture4 |
Sep 16 | PCA II (Chapter 7) | lecture5 |
Sep 19 | High Dimensional Data (Chapter 6) | lecture6 |
Sep 23 | Pattern Mining I (Chapter 8) | lecture7 |
Sep 26 | Pattern Mining II (Chapter 9) | lecture8 |
Sep 30 | Representative-Based Clustering I (Chapter 13) | lecture9 |
Oct 03 | Representative-Based Clustering II (Chapter 13) | lecture10 |
Oct 07 | Density-based Clustering (Chapter 15) | lecture11 |
Oct 10 | Spectral Clustering (Chapter 16) | lecture12 |
Oct 14 | NO CLASS (Columbus Day) | |
Oct 17 | EXAM I | |
Oct 21 | Bayes Classifier, Discriminant Analysis (Chapters 18, 20) | lecture13 |
Oct 24 | Associative Memories (Bao Pham) | |
Oct 28 | Discriminant Analysis, Linear Regression I (Chapters 20, 23) | lecture14 |
Oct 31 | Linear Regression II (Chapter 23) | lecture15 |
Nov 04 | Logistic Regression (Chapter 24) | lecture16 |
Nov 07 | Support Vector Machines I (Chapter 21) | lecture17 |
Nov 11 | Support Vector Machines II (Chapter 21) | |
Nov 14 | Neural Networks I (Chapter 25) | |
Nov 18 | Neural Networks II (Chapter 25) | |
Nov 21 | Deep Learning I (Chapter 26) | |
Nov 25 | Deep Learning II (Chapter 26) | |
Nov 28 | NO CLASS (Thanksgiving) | |
Dec 02 | Classification Assessment (Chapters 22) | |
Dec 05 | Regression Assessment (Chapter 27) | |
Dec 09 | EXAM II |