Information Retrieval in High Dimensional Data

This project lists all of the deliverables for the TUM Information Retrieval in High Dimensional Data class (Summer Semester 2018). Below is the outline and links to demo IPython Notebooks for each deliverable.

Deliverables

Assignments

• Assignment 1: Curse of Dimensionality & Statistical Decision Making
• Assignment 2: Principal Component Analysis (PCA) & k-Nearest Neighbors (k-NN)
• Assignment 3: The Kernel Trick & Kernel PCA

Laboratories

• Lab 01: NumPy Basics
• Lab 02: Statistical Decision Making & k-Nearest Neighbors (k-NN)
• Lab 03: Word Embeddings
• Lab 3.5: Logistic Regression Theory
• Lab 04: Logistic Regression
• Lab 4.5: Projections, EVD & SVD Theory
• Lab 05: Principal Component Analysis (PCA)
• Lab 06: Neural Networks Theory
• Lab 07: Convex Optimization
• Lab 7.5: Kernel PCA Theory
• Lab 08: Support Vector Machine (SVM)
• Lab 8.5: Convex Optimization Theory
• Lab 09: Linear Discriminant Analysis (LDA)

Poster Presentation

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings *

* paper link: arxiv.org/abs/1607.06520

Datasets

• yaleBfaces.zip ¹ (assignment 2)
• mnist.zip ² (assignment 3, lab 2, lab 5, lab 8, lab 9)
• imdbSentiment.zip ³ (lab 3, lab 4)
• visTex.zip ⁴ (lab 5)

¹ source: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/Yale%20Face%20Database.htm
² source: http://yann.lecun.com/exdb/mnist/
³ source: http://ai.stanford.edu/~amaas/data/sentiment/
⁴ source: http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

Additional Material

• Course Cheat Sheet

Versioning

We version the project with each new deliverable. For the versions available, see the tags on this repository.

Authors

• Akbar, Uzair - [email protected]
• Decker, Thomas - [email protected]
• Hertel, Nico - [email protected]
• Liao, Zhenchen - [email protected]
• Siriya, Seth - [email protected]

See also the list of contributors.

Acknowledgments

• Prof. Dr. Martin Kleinsteuber
• Alexander Sagel