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
- 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|