General Edit on GitHub
- Facebook Group Chat
- Year 4 Google Drive
- mailing list archives - ug4-students
- Useful YouTube channel for MLPR / PMR / IT / IAML / DME
- You can manage citations with JabRef or Zotero
- Use DBLP to generate .bib citations over Google Scholar
- Write your Intro, Conclusion and Abstract last — your project might change by the time you’ve written everything else.
- Abstract: The advertisement for your paper. You want to start with a very general scope and narrow down to specifics very quickly. Don’t use jargon. Do flex your results.
- Introduction: Similar in that you want to be general, but you have more space. You need to talk about the Motivation, Objective, Contribution and Organisation.
- Conclusion: An overview of the project and your results. You’ll want to have “Critical evaluation of own work” in here and also discuss how the project has helped you (the latter isn’t in the marking scheme however was advised to include this).
- Ask your supervisor if you can use “I” and what tense to use. My supervisor banned “I” and mandated past tense, YMMV.
- Outstanding undergraduate projects - Recent dissertations which have scored 80% and above.
- A Template-based Model for Automatic Image Description (2014), feedback, with Mirella Lapata (83%)
- WILDEBEAST: A webservice for real time characterisation of infectious disease epidemics, supervised by Andrew Rambaut (80%)
ADBS | drps, info, papers April/May exam Edit on GitHub
AGTA | drps, info, papers April/May exam Edit on GitHub
- Build a ray tracer from scratch (scratchapixel.com)
Aggregated solutions to questions from 2009-2012 combined here
We’ve started some notes on the course content, they can be found here.
Tree mapping to the slides here.
- All Lecture Slides in one PDF (2012/2013), (2014/2015)
- Good page on trap and emulate » Cached Copy with Comments Enabled
- Approximate Frequency Counts over Data Streams: paper, slides
- Revision notes - Started by Stefan Adamov
- Revision Notes - Mark Nemec on Github
- Docker + Pig setup, this allows to easily execute examples from Tutorials point
HCI - Human Computer Interaction | drps, info December exam Edit on GitHub
MLPR | drps, info December; December exam Edit on GitHub
PA | drps, info, papers April/May exam Edit on GitHub
TTDS - Text Technologies in Data Science | drps, info, papers April/May; April/May exam Edit on GitHub
There are some “hidden” formulas about LSH error probabilities in the lectures.
An overall studyguide for TTS 2013-2014: all lecture slides summarized added things from notabene, said in lecture, and just figured out by Sophia.
* means that this has ALL THREE OPTIONAL QUESTIONS
† means that the course is TTS and not TTDS (very old course!)
|Exam||Question 1||Question 2||Question 3|
|2019 May||Knowledge||Retrieval Models & Web||IR Evaluation|
|2018 May*||Knowledge||Retrieval Models||IR Evaluation & PageRank|
|2017 December*||Knowledge||Retrieval Models||IR Evaluation|
|2015 May*||Retrieval Models||Evaluation||PageRank & HITS|