I’d like to buy an introductory book on Machine Learning. This is not for studying, nor for reference – for that, I use books like this one. I just want to read a short book giving a bird’s eye of the Machine Learning landscape. Something like Wasserman’s “All of Statistics” for Stats. I think one of these books should do the job:

• https://www.amazon.com/dp/110845514X/ref=rdr_ext_sb_ti_hist_2
• https://www.amazon.com/Machine-Learning-Applied-Mathematics-Introduction/dp/1916081606/
• https://www.amazon.com/Hundred-Page-Machine-Learning-Book/dp/199957950X/
• https://www.amazon.com/Artificial-Intelligence-Engines-Introduction-Mathematics/dp/0956372813/

Which one would you suggest?

I was watching “The Great Hack” today and that makes it sound like Cambridge Analytica was some mind-blowing machine learning algorithm that did some amazing targeting.

The way I see it, they harvested a lot of data illegally from Facebook, but I am not sure how sophisticated or amazing their algorithm was. What are your thoughts on Cambridge Analytica purely from ML point of view? Are there any publications regarding it?

We make a system for supervised learning in Unity. You can use it in a lot of your games to experiment with AI. Nuron Dot Net is used. Also we tried AForge but got big leaning error event on simple XOR function.

Out goal is to make universe system that can be used in any kind of the games.

https://doctrina-kharkov.blogspot.com/2019/10/unity-neural-networks-tutorial.html

Let’s say I have a multi-class problem where classes are {A, B, C, Other} – where the Other class is a catch-all for all examples that are not in A, B or C.

The data comes in multiple datasets – D1 and D2. Let’s say D1 has been labeled {A, B, Other-or-C} and D2 has been labeled {A, C, Other-or-B}. In practice we can produce this kind of situation by making all C’s into Other in D1 and all B’s into Other in D2 from the original dataset D that contains all classes.

How can I modify the final layer of the network to accommodate this situation? In the end I want to train a model to predict {A, B, C, Other}

The significance of the problem is related to reducing the tagging effort. When you have D1 with 10000 examples and D2 with 500 examples, it would be much easier to train jointly with D1 and D2 as they are instead of tagging D1 with all tags in D2 and D2 with all tags in D1. Some tags might be common to D1 and D2.

This looks like a multi-task learning problem to me where the tasks are partially overlapping.

Does doc2vec still hold its weight or are there methods out there like https://openreview.net/pdf?id=SyK00v5xx that consistently outperform it?

As per Gary Marcus:

– Over-reliance on data

– Lack of generalization

– Opacity/lack of explainability

https://bdtechtalks.com/2018/02/27/limits-challenges-deep-learning-gary-marcus/

I created a tool to visualize the spectral clustering algorithm (for graphs).

What is spectral clustering?
Spectral clustering is a clustering technique that can operate either on graphs or continuous data. It makes use of the eigenvectors of the laplacian- or similarity matrix of the data to find optimal cuts to separate the graph into multiple components.

Here is the article

Here is a direct link to the tool