Machine learning consists of parametric optimization to reduce some error function on training data and then use the learnt parametric model on unseen/held-out data and evaluate. The first step is to construct the right parametric model by studying the data domain and then iterate till the performance is achieved within acceptable level. Machine learning research is highly mathematical, but you can start by using some open source ML tools and tweaking the models to get a feel for the capacities of different models.
Some topics you should familiarize are: Probability Theory, EVD/SVD, ANN, ML/MAP estimation, Minimum classification error training, SVM, LMS fitting, PCA/ICA, FSM and HMM.
If you have taken the NLP course, you can work in many interesting areas that have practical applications e.g. improving the performance of a Named Entity recognizer for web data, automatic classification of web pages into predefined categories, natural language parsing for grammar transfer in translation etc.
You should also learn some tools of the trade: regular expressions, machine learning, statistical methods, neural networks, minimum classification error training etc.
I've thought about buying the iPad keyboard and turning my linux box (an older dell) into a server where I can ssh into for coding. I ultimately decided against it because multitasking at the time didn't exist, although it was coming. Even with multitasking, it isn't as easy as on a desktop, although the new 4 finger swipe might mitigate that somewhat. Ultimately I can't rely on iOS Safari to render and execute javascript as well as the desktop counterpart, so I don't see myself doing this any time soon.
New Delhi/Remote possible. iApps.in is looking for
- Business development/marketing manager
- UI/UX designer
- NLP researcher
iApps.in is a semantic search and discovery engine for the App Store that combines the social, semantic and mobile internet technologies to connect users with the apps they want.
For more details see http://iapps.in/jobs
Everyday Apple approves around 900 NEW iOS and 50 new Mac Apps in the App Store[1]. In such a huge market, it is quite likely that some would try to game it.
The human curation approach falls flat on such a scale. Machine learning and natural language processing can help us in mining the App Store to detect anomalous behavior and improve the search and discovery of apps.
The statistical models of temporal distributions of ratings and rankings are still emerging and such hightlighting provide a useful resource to train the models. So if you see something, say something.
I wonder if we'll see Google doing a significantly better job in the Android store?
This is an obvious extension to search engine optimization, and I'll bet the guys doing well at it are using many similar techniques to website optimizes.
Goggles web spam team can probably give some great advice to the "app spam team".
It is indeed an extension of the "Website optimization for the search engine" or SEO as they call it. To a search engine developer, SEO means to improve the search engine for better output.
The fight against spam is a constantly evolving one and though some of the techniques from "web" search engine could be applied to the App store, there are unique differences between the two - e.g. the rating, download rankings are not directly analogous to page rank. So Google does seem to have some advantage, but the App store problem will need a fresh research approach.
