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Course
| Code: | JMSC 6041 |
| Subject: | Journalism |
| Views: | 15,070 |
Computational Journalism
Course Description
Computational Journalism is a course given at JMSC during the Spring 2013 semester. It covers, in great detail, some of the most advanced techniques used by journalists to understand digital information, and communicate it to users. We will focus on unstructured text information in large quantities, and also cover related topics such as how to draw conclusions from data without fooling yourself, social network analysis, and online security for journalists. These are the algorithms used by search engines and intelligence agencies and everyone in between.
Lecture 3: Algorithmic Filters
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Video Lectures & Study Materials
Visit the official course website for more study materials: http://courses.jmsc.hku.hk/jmsc6041spring2013/author/jstray/
| # | Lecture | Play Lecture | Notes & Slides |
|---|---|---|---|
| 1 | Basics of Computational Journalism: Feature Vectors, Clustering, Projections (2:29:56) | Play Video | Lecture Slides |
| 2 | Text Analysis: Tokenization, TF-IDF, Topic Modeling (2:02:37) | Play Video | Lecture Slides |
| 3 | Algorithmic Filters: Information Overload (2:04:17) | Play Video | Lecture Slides |
| 4 | Social and Hybrid Filters: Collaborative Filtering (2:10:39) | Play Video | Lecture Slides |
| 5 | Social Network Analysis: Centrality Algorithms (2:29:20) | Play Video | Lecture Slides |
| 6 | Knowledge Representation: Structured data & Linked open data (2:09:17) | Play Video | Lecture Slides |
| 7 | Drawing Conclusions from Data (2:34:03) | Play Video | Lecture Slides |
| 8 | Security, Surveillance, and Privacy (1:38:57) | Play Video | Lecture Slides |
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