16
votes
Accepted
What are the mathematical prerequisites for an AI researcher?
Good Mathematics Foundation
Begin by ensuring full competency with intermediate algebra and some other foundations of calculus and discrete math, including the terminology and basic concepts within ...
11
votes
Accepted
What are some resources on computational learning theory?
Although I have only partially read or not read at all some of the following resources and some of these resources may not cover more advanced topics than the ones presented in the book you are ...
- 41.1k
10
votes
What are the mathematical prerequisites for an AI researcher?
I work as a professor, and recently designed the mathematics requirements for a new AI major, in consultation with many of my colleagues at other institutions.
The other answers, particularly this one ...
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6
votes
Accepted
What are other examples of theoretical machine learning books?
Some of the books that you mention are often used as reference books in introductory courses to machine learning or artificial intelligence.
For example, if I remember correctly, in my introductory ...
- 41.1k
6
votes
What is the difference between the US and global edition of the AIMA book by Russell and Norvig?
I check again with the subchapters of Artificial Intelligence: A Modern Approach, 4th Global ed / US ed from this website the pdf subchapters reference of Global Edition and US Edition. I can confirm ...
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5
votes
Accepted
What does "statistical efficiency" mean in this context?
Statistical efficiency in this context essentially means that a CNN would require fewer training examples than a fully connected network to learn. Intuitively this seems reasonable: more parameters to ...
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5
votes
What are some alternatives to the book "Artificial Intelligence: A Modern Approach"?
Why is AIMA dense?
Artificial intelligence is a broad field: that's why Artificial Intelligence: A Modern Approach (AIMA) may look a bit dense to newcomers, given that it covers many different aspects ...
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4
votes
What are some alternatives to the book "Artificial Intelligence: A Modern Approach"?
The following 2 books helped me understand the basics and guided me through my first AI / CI implementations.
Computational Intelligence: An Introduction by Andries P. Engelbrecht
It includes the ...
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4
votes
What are some intermediate or advanced books on neural networks?
Neural Network Design (2nd edition) by Hagan et al. is one resource you could look at. It's a huge tome, weighing in at over 1000 pages in pdf form, but it is freely available (you can also buy a dead-...
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4
votes
Which books or papers clearly explain the relation between Ising models and deep neural networks?
The following articles
Ising models for networks of real neurons (2006) by Gasper Tkacik et al.
Deep neural networks for direct, featureless learning through observation: The case of two-dimensional ...
- 555
4
votes
What are some books or state of the art papers about the development of a strong-AI?
There is actually a book called Artificial General Intelligence by Ben Goertzel and Cassio Pennachin. It's a bit out of date (from 2008), and published as a Springer-Verlag monograph (which tends to ...
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4
votes
Accepted
Are the Dot Product and Tensor Product the same thing in Machine Learning?
In machine learning, a tensor is a multidimensional array with some operations. In mathematics, the definition of a tensor is slightly different (see the Wikipedia article). However, the definitions ...
- 41.1k
3
votes
Accepted
How is the state-value function expressed as a product of sums?
A quick review of resolving expectations: If you know that a discrete random variable $X$, drawn from set $\mathcal{X}$ has probability distribution $p(x) = \mathbf{Pr}\{X=x \}$, then
$$\mathbb{E}[X] =...
- 32.9k
3
votes
What introductory books to reinforcement learning do you know, and how do they approach this topic?
In addition to the ones you mentioned, I would add Algorithms of Reinforcement Learning by Csaba Szepesvári. There is a number of professors who use it as a reference in their RL teaching materials (...
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3
votes
What are other examples of theoretical machine learning books?
Pattern Recognition And Machine Learning is a great theoretical book. I don't know anything better on standard ML. I read several pages from it myself and all my colleagues researchers suggest to look ...
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3
votes
Accepted
Which linear algebra book should I read to understand vectorized operations?
If you already have two years of a bachelor's of mathematics, I recommend part I of the book that you're mentioning. That part of the book reviews the main mathematics used in the optimization of ...
3
votes
Accepted
Which rules should I define for the predicate "not_to_far" of the exercise 1.1 of the book "Simply Logical: Intelligent Reasoning by Example"?
Your intuition is good. Because "nearby" is only defined with "connected", there could only be 1 station between them. However, it says that the stations are "not_too_far" if at most one station is ...
- 3,847
3
votes
What are the mathematical prerequisites for an AI researcher?
As far as simple algorithms like Gradient Descent are concerned, you need to have a good grasp of partial derivatives. Especially if you want to implement neural networks.
Also most algorithms are ...
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3
votes
Example of lemma having multiple boldface forms
It is very confusingly worded, and I would think it's incorrect according to linguistic terminology.
A lemma is the canonical form of a word, commonly the infinitive of a verb, the nominative singular ...
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2
votes
Is Christopher Bishop's "Pattern Recognition and Machine Learning" out of date in 2018?
This book is still relevant today!
It describes many ML concepts, such as linear regression, neural networks, support vector machines, Gaussian processes, probabilistic graphical models, variational ...
- 41.1k
2
votes
What are some books/papers that deal with fundamental and philosophical issues of ML and relate it to the global discourse of AIs?
I'll recommend two sources:
The venerable Russell & Norvig book, which is a common text in AI courses. Russell & Norvig end each chapter with a summary of the history of the developments of ...
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2
votes
Accepted
What does the notation $[m]=\{1, \ldots, m\}$ mean in the equation of the empirical error?
This is a commonly used notation in theoretical computer science.
$[m]$ is not the variable $m$, but is instead the set of integers from $1$ to $m$ inclusive. The empirical error equation thus reads ...
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2
votes
Which part of "Perceptrons: An Introduction to Computational Geometry" tells that a perceptron cannot solve the XOR problem?
The section of the book Perceptrons: An Introduction to Computational Geometry (expanded edition, third printing, 1988) that shows the limitations of the perceptron should be 11.8 The Nonseparable ...
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2
votes
What introductory books to reinforcement learning do you know, and how do they approach this topic?
The (draft) book Reinforcement Learning: Theory and Algorithms, by Sham M. Kakade (who published a natural policy gradient algorithm and other important research) and others, introduces RL in a ...
- 41.1k
2
votes
Why do we commonly use the $\log$ to squash frequencies?
It's much easier to deal with logarithms, as the relevant numbers are usually very small or very large. If you have a long exponential expression, it's hard to see the difference, but if you're ...
- 5,397
2
votes
What are the Calculus books recommended for beginner to advanced researchers in artificial intelligence?
Answer: Calculus James Stewart is the best for a beginner.
I started to learn Calculus studying engineering with James Stewart Calculus ( maybe the best for beginners and is really didactic ), ...
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2
votes
Which formula of p(x, y) to use?
In the book you mentioned, the author is dealing with classification, that is, inferring the label $y$ from a sample $x$. Let $X$ and $Y$ be the corresponding random variables. $p(y|x)$ can be ...
2
votes
Mathematics books for reinforcement learning
Two books that really helped me to understand RL/deep RL are:
Reinforcement Learning: An Introduction by Andrew Barto and Richard S. Sutton
Deep Reinforcement Learning: Fundamentals, Research and ...
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2
votes
Clarification on why Deep Learning works from Goodfellow's book
In general if the function behaves differently in different regions of the input space of high dimensions, it becomes hopeless to describe it with limited data. The number of examples needed to ...
- 3,302
1
vote
What is meant by "real-valued argument" in this context of the convolution operation?
In its most raw form, convolution is defined as: $(f*g)(t) = \int_{-\infty}^\infty f(\tau) \cdot g(t-\tau) d\tau$.
Here, t doesn't represent the time domain. Infact, it represents the real valued ...
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