Machine learning

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What is the difference between AI and machine learning

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Announcing new online courses

What is better way to share knowledge with wider audience then creating an online courses. For a while, I have been thinking about this idea and how to effectively share knowledge. Finally, I can announce that I am about to build a small online school with the set of low-cost/high-value courses in cyber-security, natural language processing and machine learning!

The first two courses are already online and ready to accept students. The first courses that I have created are on Information security and Malware analysis. For these courses, I already had materials, as I was teaching them in quite formal settings. Therefore, they have been transferred to Udemy format. More courses will be coming in the following months.

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Ideas for the future

I would like to state a couple of ideas that I have been thinking in the past number of days regarding what I do, which generally is natural language processing and machine learning. They may be something I am already working on and some ideas for the future and future directions. Only time will tell which I will manage to tackle.

Named Entity recognition

Interesting topic, however, it seems it is moving more towards industry domain, rather than academia. However, there are still a lot of people working on this, especially in specialized domains, such as biomedicine. Lately, we are having at the University one big project related to anonymization of clinical health records and lab reports. As a first

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Starting Inspiratron talks podcast

For a while, I have been preparing for this step. This blog got it’s audio podcast show and it is called Inspiratron talks. First I will give some links and then talk about mission and vision of the podcast. Inspiratron talks is hosted at the moment on SoundCloud:

At the moment, two episodes can be found. However, I am hoping to release one episode per week during the season, however, there may be some breaks around the Christmas and during the summer.

You can listen to podcasts using the following Podcast directories:

www.stitcher.com/podcast/inspirat…talks?refid=stpr
www.acast.com/inspiratrontalks
www.blubrry.com/inspiratron/

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Awarded best paper award on NLDB 2018 conference

A paper called “Classification of Intangible Social Innovation Concepts” that was submitted and accepted for presentation at 23rd International Conference on Natural Language & Information Systems (NLDB2018) and was held in Paris, France from 13th to 15th June 2018, received one of the best paper award. In total 3 papers were awarded as the best papers with no ranking or order between them. Papers also received monetary award.

NLDB is quite established (organised already for 23 years) and good conference in the area of natural language processing. Usually about 15-18% of papers submitted are accepted as long papers. It seems like some more papers are accepted as short papers and poster presentations, so the percentages of accepted papers is higher, but

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Impressions about Quantopian

Usually I don’t write about services I use, but one came into my way that I like – Quantopian. For a while I am interested in financial side of computer science, quantitative analysis, machine learning applications to predicting a stock market, etc. However, I was not really able to get grasp of these fields. I certainly could get some data from either Yahoo Finance and I even had access to Bloomberg terminal to play a bit with at the University library. I made some models, published a paper or ArXiv , made a tool that make these models available and published it on GitHub and I am talking with people at MUTIS Finance society to make a Quant

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FinAnalizer – Tool for financial analysis of stocks

For a while I was playing with financial data and financial data mining. I have already written about it in a separate post. Now, I decided to try to make my research so far accessible a bit more, so people can use it. Basically, what I previously did is that I used a bunch of technical financial parameters, such as price, P/E ratio, Price to Sales, Price to Book and many other ratios to try to create a model that will be able to predict a stock price movement over the long term (1 year period). It seems that it is working for about 75% of times using machine learning, namely Random Forests algorithm. For more details you can

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How to predict future movement of stock prices using machine learning

For a while, I have been interested in finance and especially in algorithmic trading. I had this question over my head for some time “Can the long term investment be helped by machine learning?”. The answer seems to be yes based on many models we currently have for company evaluation, especially when it comes to evaluating intrinsic value of the company and comparing it to the market price. Benjamin Graham set a set of criteria, which are pretty much numerical and require analyst to look at couple of financial ratios. Graham criteria are hard to apply in today’s market because they are too harsh. But the question is, can we learn new criteria by using historical data?

OWASP Seraphimdroid

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New version of OWASP Seraphimdroid (v2.0) is published

Dear users and security aware people, we have a great announcement. The new version of OWASP Seraphimdroid is published with some very interesting breakthrough features. If you liked OWASP Seraphimdroid before, now you will probably love it. We have improved machine learning aided permission scanner, new settings scanner, improved SMS interceptor, improved application locker, and some more. OWASP organized OWASP Code Summer Sprint, where OWASP Seraphimdroid participated as one of the project. Student that was proposed some and was selected to develop improvements on OWASP Seraphimdroid was Kartik Kohli. I had opportunity to mentor him as OWASP Seraphimdroid project leader. So let’s start explaining the major improvements.

If you are not aware or do not know what features

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Expirience from Lisbon Machine Learning Summer School

I have participated on Lisbon Machine Learning Summer School (LxMLS), which took place on July 16-23  at Instituto Superior Técnico, a leading Engineering and Science school in Portugal. It is organized jointly by IST, the Instituto de Telecomunicações and the Spoken Language Systems Lab – L2F of INESC-ID. It was quite a great experience, on the one side to see Lisbon, while on the other learn a bit from the best people in Machine Learning and Natural Language Processing and meet fellow PhD students who work in the same or similar area as I do around the Europe. I will just briefly tell the experience.

I arrived to Lisbon day before, on 15th July. That evening we already could