My name is Anton Antonov. This blog is for examples and descriptions of real world applications of machine learning and other prediction algorithms implemented in Mathematica.

I was a kernel developer at Wolfram Research, Inc. for seven years working, mostly, on numerical algorithms. (I implemented and documented the framework and integrators of NIntegrate.) During the last 9+ years I designed, prototyped, and implemented a variety of machine learning and data mining algorithms.

This blog is strongly associated with

  1. the Mathematica source code project MathematicaForPrediction at GitHub, and

  2. the Mathematica and R source code project MathematicaVsR at GitHub.

8 thoughts on “About

  1. Hi Anton,
    I used to work for Wolfram Solutions and now I’m back to consulting. I have developed a Mathematica Application for data analysis (link to screencast: http://youtu.be/RhYoWRT2yPk) which may at some point be of interest to you. Please take a look at the screencast and keep my email in case you see any synergistic opportunity in the future.

  2. Dear Anton Antonov, I was search some codes to estimate quantile regression through linear programming without penalty and with penalty (LASSO and SCAD), I read many information from your website..I can not download the Pdf file from mathematica for prediction at GitHub. can you forward me that file if possible also send me some codes. thanks… I am a PhD student

  3. Hello Anton
    I am working on forecast algorithms in Mathematica, similar works like yourself, would like to know if we could use your services or cooperate somehow.


  4. Hey, Anton. What would you call a conversational engine and how would you implement it?

    Let me explain: I’m writing a conversational engine/bot. I’m new to the field and having to learn the specific names used.

    My bot has a defined number of capabilities (e.g. two: finding gifs and printing jokes). After parsing the first user given string – using parser generators, thanks for that!! -, I know what the user is trying to do (either requesting gifs or requesting jokes). Hence, depending on the intention/capability, the bot asks different questions (e.g. “gif of what?” for gifs, but “dirty or clean?” for jokes).

    As you can see, the conversation may follow one of the defined paths. I’ve been thinking about using a Finite State Machine for that (i.e. for the conversational “engine”), but I’m not happy with it. Questions:

    What would you call this “conversational/context engine”?
    How would you suggest implementing this?

    Thank you in advance and thanks for these awesome posts as well. Very insightful!


    • Hi Gunar,

      Thank you for your note!

      What would you call this “conversational/context engine”?

      Well you can call it whatever you like… More seriously, I am not sure what kind of naming you are looking for. For example, one type of name is “Joke-gif bot”, another is “interaction program for user queries of jokes and images.”

      How would you suggest implementing this?

      I think I have already provided an answer to this question with this blog post of mine:

      “Creating and programming DSLs”.

      Best regards,

  5. Hi Anton, I am a researcher working in the field of financial applications of Self organizing maps, and I wonder if there is any room for cooperation between we two.
    Thanks in advance for your reply,

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