Ebrahim Alareqi's Blog

You should publish in communities such as WWW, SIGIR, WSDM, RecSys, CHI, KDD, AAAI, ACL, NIPS, ICML.

Follow up on our discussion regarding what you should have worked on before graduation: Discrete mathematics Computer science theory (automata theory, lexical analysis, etc) Know a programming language very well (all the famous packages in that language including the data structure ones and the string manipulation ones too beside the basic language function) Beside data structure, most major algorithms (sorting, tracing, etc.) You need to know web development stack if you will be doing that in the future. These are the things I believe are important for you. Once you master these skills, you need to put them in action in form of real-world projects. I said real project because remember the environment and deadlines are our motivation. This is a comprehensive list of all the topics and subtopics in computer science. Check what's missing. You don't to be perfect in all of them, but you need to master at least one: https://en.wikipedia.org/wiki/Outline_of_computer_scien...

Here's an exhaustive list of learning resources (in no particular order): ==================================== 1) Linear Algebra: (used in machine learning (& deep learning) to understand how algorithms work under the hood. Basically, it's all about vector/matrix/tensor operations, no black magic is involved!) - Khan Academy Linear Algebra series (beginner friendly) (https://www.khanacademy.org/math/linear-algebra) - Coding the Matrix course (and book) (http://codingthematrix.com/, http://academictorrents.com/details/54cd86f3038dfd446b037891406ba4e0b1200d5a) - 3Blue1Brown Linear Algebra series (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) - fastai Linear Algebra for coders course, highly related to modern ML workflow! (https://github.com/fastai/numerical-linear-algebra/blob/master/README.md) - First course in Coursera Mathematics for Machine Learning specialization (https://www.coursera.org/specializations/mathematics-machine-lear...

Notes from  Jeff Spies  webinar: From: https://www.ideals.illinois.edu/handle/2142/101880 Abstract: We're at an important stage in the history of science. The internet has dramatically accelerated the pace and scale of communication and collaboration. We have the computational resources to mine and discover complex relationships within massive datasets from diverse sources. This will usher in a new era of knowledge discovery that will undoubtedly lead to life-saving innovation, and access to content is paramount. But how do we balance transparency and privacy or transparency and IP concerns? How do we protect data from being selectively deleted? How do we decide what to make accessible with limited resources? How do we go from accessible to reusable and then to an ecosystem that fosters inclusivity and diversity? And what if we no longer own the content we'd like to be made accessible? Such is the case with most journal articles. Skewed incentives have developed around c...

Most of the things done on mobile analytics are geared toward user behavior. Companies invest heavily on this aspect of analytics. Mobile analytics is the practice of collecting user behavior data, determining intent from those metrics and taking action to drive retention, engagement, and conversion. The field includes the mobile web, but tends to focus on analytics for native iOS and Android applications. Analysis that used to happen in Excel and SQL has largely been replaced by a handful of tools that make adhering to analytics best practices significantly easier. Consumer and business applications tend to face the same set of challenges in their mobile marketing and retention, so this guide is designed to address both. To actually start collecting data about your users, gathering insights, and executing on them, you need the right tools. Here are five that we recommend for any high-performance mobile analytics toolchain. Segment - for tracking events and moving data Reds...

The development of the Learning Pyramid in the 1960’s — widely attributed to the NTL Institute in Bethel, Maine— outlined how humans learn. As research shows, it turns out that humans remember: 5%  of what they learn when they’ve learned from a  lecture (i.e. university/college lectures) 10%  of what they learn when they’ve learned from  reading (i.e. books, articles) 20%  of what they learn from  audio-visual (i.e. apps, videos) 30%  of what they learn when they  see a demonstration 50%  of what they learn when engaged  in a group discussion. 75%  of what they learn when they  practice what they learned. 90%  of what they learn when they  use immediately (or teach others) Is this something you're interested in?   Find out how to remember 90% of everything you learn.