16

Dennis Soemers' answer is correct: you should use a HashSet or a similar structure to keep track of visited states in BFS Graph Search. However, it doesn't quite answer your question. You're right, that in the worst case, BFS will then require you to store 16! nodes. Even though the insertion and check times in the set will be O(1), you'll still need an ...


14

Artificial Intelligence is a very broad field and it covers many and very deep areas of computer science, mathematics, hardware design and even biology and psychology. As for the math: I think calculus, statistics and optimization are the most important topics, but learning as much math as you can won't hurt. There are many good free introductory resources ...


10

Start with Andrew Ng's introduction to Machine Learning course on Coursera. There are not many prerequisites for that course, but you will learn how to make some useful things. And, more importantly, it will clearly show you which subjects you need to learn next.


10

A neuron is said activated when its output is more than a threshold, generally 0. For examples : \begin{equation} y = Relu(a) > 0 \end{equation} when \begin{equation} a = w^Tx+b > 0 \end{equation} Same goes for sigmoid or other activation functions.


9

To excel in in AI you need a mathematical intuition or point of view. In order to become a full stack AI engineer, it is important that you have a firm understanding of the mathematical foundations of machine learning. My advice to anyone preparing to jump into the field is that learning mathematics is about doing. Remember the 20/80 rule. You need to ...


8

A really good introduction is the Berkeley CS188 class videos and projects. You can find those materials at http://ai.berkeley.edu/home.html You probably also want to get ahold of a copy of Artificial Intelligence: A Modern Approach by Norvig and Russell. For more on the "machine learning" aspects of AI, including an introduction to Neural Networks, take ...


8

You can use a set (in the mathematical sense of the word, i.e. a collection that cannot contain duplicates) to store states that you have already seen. The operations you'll need to be able to perform on this are: inserting elements testing if elements are already in there Pretty much every programming language should already have support for a data ...


7

Supervised learning is typically an attempt to learn a mathematical function, $f(\bf X)=\bf y$. For this, you need both the input vector $\bf X$ and the output vector $\bf y$. The model outputs have whatever dimensionality that the target values have. Unsupervised learning models instead learn a structure from the data. A clustering model, for example, is ...


7

While the answers given are generally true, a BFS in the 15-puzzle is not only quite feasible, it was done in 2005! The paper that describes the approach can be found here: http://www.aaai.org/Papers/AAAI/2005/AAAI05-219.pdf A few key points: In order to do this, external memory was required - that is the BFS used the hard drive for storage instead of RAM....


7

Utility is a fundamental to Artificial Intelligence because it is the means by which we evaluate an agent's performance in relation to a problem. To distinguish between the concept of economic utility and utility-based computing functions, the term "performance measure" is utilized. The simplest way to distinguish between a goal-based agent and a utility-...


6

Backpropagation is a subroutine often used when training Artificial Neural Networks with a Gradient Descent learning algorithm. Gradient Descent requires computation of the error gradient, i.e. derivatives, of a cost function with respect to the network parameters. BP allows you to find this gradient a lot faster than using naive methods. Reinforcement ...


6

Neural networks seem to be (something along the lines of) a type of algorithm that creates a graph which works based on a theory about how neurons interact, in order to create self-learning programs. Technically, a neural network is a combination of group of high dimensional arrays/vectors storing 'weights' (more on this soon) a list of instructions or ...


6

In a neural network (NN), a neuron can act as a linear operator, but it usually acts as a non-linear one. The usual equation of a neuron $i$ in layer $l$ of an NN is $$o_i^l = \sigma(\mathbf{x}_i^l \cdot \mathbf{w}_i^l + b_i^l),$$ where $\sigma$ is a so-called activation function, which is usually a non-linearity, but it can also be the identity ...


5

This is fairly boilerplate advice, but, since you're brand new to AI, I'd personally suggest writing a classical Tic-Tac-Toe AI, ideally using minimax. I suggest this because minimax is fundamental to AI, and there are many webpages devoted to this subject, such as How to make your Tic Tac Toe game unbeatable by using the minimax algorithm and Tic Tac Toe: ...


5

You'll find that both Calculus and Linear Algebra have some application in AI/ML techniques. In many senses, you can argue that most of ML reduces to Linear Algebra, and Calculus is used in, eg. the backpropagation algorithm for training neural networks. You'd be well served to take a class or two in probability and statistics as well. Programming ...


5

AI is quite large in scope and it sits at the intersection of several areas. However, there are a few essential fields or topics that you need to know Set theory Logic Linear algebra Calculus Probability and statistics I would recommend you to first explore the AI algorithms that you might be interested in. I advise you to start with machine learning and ...


5

Well, you are definitely mixing two different things. Here are those bits: The function that deep learning approximates is basically a function that best fits the INPUT DATA points. You should not think about its differentiability or optimization aspects. We don't care what type of function it is; we just want the best fit of input data (ofcourse ...


5

As it can be easily pointed out that true random numbers cannot be generated fully by programming and some random seed is required. This is true. In fact, it is impossible to solve using software. No software-only technique can generate randomness without an initial random seed or support from hardware. This is also true for AI software. No AI design that ...


5

Using a machine learning or AI-powered model once it has been built and tested, is not directly an AI issue, it is just a development issue. As such, you won't find many machine learning tutorials that focus on this part of the work. But they do exist. In essence it is the same as integrating any other function, which might be in a third-party library: ...


4

I would suggest you to start with Andrew Ng's Machine Learning course on Coursera. He provides the brief introduction to mathematics necessary for machine learning. Though not complete, it will be enough to cruise through the course. Next carefully learn logistic regression in the course. The sigmoid function will be widely used in neural networks. In the ...


4

When I got interested in AI, I started with the most basic things. My very first book was Russell&Norvig's Artificial Intelligence- A modern Approach. I think that's a good place to start, even if you're mostly interested in Deep Nets. It treats not just the basic AI concepts and algorithms (expert systems, depth-first and breadth-first search,knowledge ...


4

When crossover happens and one parent is fitter than the other, the nodes from the more fit parent are carried over to the child. This is the case as disjoint and excess genes are only carried over from the fittest parent. Here's an example: // Node Crossover Parent 1 Nodes: {[0][1][2]} // more fit parent Parent 2 Nodes: {[0][1][2][3]} Child Nodes: {[0]...


4

In your question you didn’t specify the type of pooling that you aren’t doing. So it’s possible that you could have, for example a mean pool followed by a max or min pool. What this could do is combine the idea of reducing dimensionality of your data from a holistic perspective with the mean pool and then choosing the best of your averages with your max ...


4

The term "activated" is mostly used when talking about activation functions which only outputs a value (except 0) when the input to the activation function is greater than a certain treshold. Especially when discussing ReLU the term "activated" may be used. ReLU will be "activated" when it's output is greater than 0, which is also when it's input is greater ...


4

In many cases, a production-ready model has everything it needs to make predictions without retaining training data. For example: a linear model might only need the coefficients, a decision tree just needs rules/splits, and a neural network needs architecture and weights. The training data isn't required as all the information needed to make a prediction is ...


4

What you have could be well described as a Task Allocation problem, which is studied as part of the planning subfield of AI. Chapters 10 & 11 of Russell & Norvig provide a good overview of this area, although I think they don't talk too much about Task Allocation in particular. There are two basic approaches to this problem: centralized approaches, ...


4

The term you are looking for is stylometry, which is related to a technique in forensic linguistics called writeprint analysis. There are many different techniques to perform stylometric analysis, from the very basic 5-feature analysis classifying features such as the lexicon and idiosyncrasies unique to a person to more complex analysis utilizing neural ...


4

Welcome to AI.SE @Kate_Catelena! I teach AI courses at the undergraduate level, and so have seen a lot of semester projects over the years. Here are some templates that often lead to exciting outcomes: Pick a new board or card game, and write a program to play it. Your course has probably covered Adversarial Search, and may also have covered Monte Carlo ...


4

I think the key part of your question is "as a beginner". For all intents and purposes you can create a state of the art (SoTA) model in various fields with no knowledge of the mathematics what so ever. This means you do not need to understand back-propagation, gradient descent, or even mathematically how each layer works. Respectively you could just ...


3

Here are some resources I have found useful to get to know the basics of AI Andrew Ng's lecture series on AI Andrew Ng's lecture at the Stanford Business School Andrew Ng - The State of Artificial Intelligence Andrew Ng is a visiting professor at Stanford, founder of Coursera and currently the head of research at Alibaba. The above videos should give you (...


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