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Lecture #1 Supplemental Material: Compendium of Drawing Methods for Trees
Lecture #1 [Reingold and Tilford 1981] Tidier Drawings of Trees
Lecture #1 [Supowit and Reingold 1983] The complexity of drawing trees nicely
Lecture #2 Web demo for force-directed approaches
Lecture #4 [Schnyder 1990] Embedding Planar Graphs on the Grid
Lecture #5 [Patrignani 2001] On the complexity of orthogonal compaction
Lecture #8 [de Fraysseix, de Mendez, Rosenstiehl 1994] On Triangle Contact Graphs
Lecture #8 [He 1993] On Finding the Rectangular Duals of Planar Triangular Graphs
Lecture #8 [Kant and He 1994] Two algorithms for finding rectangular duals of planar graphs
Lecture #9 [CGGKL18] The Partial Visibility Representation Extension Problem
Lecture #10 [Székely 1997] Crossing numbers and hard Erdős problems in discrete geometry
Lecture #10 [Bienstock and Dean 1993] Bounds for rectilinear crossing numbers
Lecture #10 [Schaefer 2020] The Graph Crossing Number and its Variants: A Survey
Lecture #10 Terry Tao's blog on Crossing Numbers
Lecture #10 Movie "N Is a Number: A Portrait of Paul Erdös"
Lecture #12 [Nöl14] A Survey on Automated Metro Map Layout Methods
Lecture #1-12: Bonus Summary
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SS23_IR
Lectures
Lecture 11: IR Evaluation
Lecture 11: IR Evaluation
Completion requirements
Click
11-Evaluation.pdf
link to view the file.
◄ Lecture 10: Neural Learning to Rank
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Lecture 1: Introduction to IR
Lecture 2: Boolean Retrieval & Inverted Index
Lecture 3: Data Structures & Tolerant Retrieval
Lecture 4: Term Weighting & Vector Space Model
Lecture 4: VSM (recording of the online session)
Lecture 5: Classic Probabilistic Retrieval
Lecture 6: Language Modeling for Information Retrieval
Lecture 7: Relevance Feedback & Query Expansion
Lecture 8: Latent & Semantic Retrieval
Lecture 9: Classification, Clustering and Learning to Rank
Lecture 9: Classification, Clustering, and Learning to Rank (online lecture recording)
Lecture 10: Neural Learning to Rank
Lecture 12: Link Analysis in Web Search
Submission of project presentation and code
Exercise 01
Exercise 01 - Solution
Projects - Intro
Exercise 02
Exercise 02 - Solutions
Exercise 03 - Relevance Feedback & Semantic Retrieval
Exercise 03 - Solutions
Exercise 04 - Machine Learning, Evaluation & PageRank
Exercise 04 - Solutions
Results of the second exam (11.10.2023)
Lecture 12: Link Analysis in Web Search ►
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