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Machine learning has been defined by many people in multiple (often similar) ways [1, 2]. One definition says that machine learning (ML) is the field of study that gives computers the ability to learn without being explicitly programmed. Given the above definition, we might say that machine learning is geared towards problems for which we have (lots of) data ...


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Machine learning is a subset of artificial intelligence. Roughly speaking, it corresponds to its learning side. There is no "official" definitions, boundaries are a bit fuzzy.


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Artificial intelligence According to the book Artificial Intelligence: A Modern Approach (section 1.1), artificial intelligence (AI) has been defined in multiple ways, which can be organized into 4 categories. Thinking Humanly Thinking Rationally Acting Humanly Acting Rationally Figure 1.1 (of the same book) contains 8 definitions (by renowned people like ...


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This is a fundamentally a philosophical question. What makes AI AI? But first things, why would DFS be considered an AI algorithm? In its most basic form, DFS is a very general algorithm that is applied to wildly different categories of problems: topological sorting, finding all the connected components in a graph, etc. It may be also used for searching. For ...


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The machine learning is a sub-set of artificial intelligence which is only a small part of its potential. It's a specific way to implement AI largely focused on statistical/probabilistic techniques and evolutionary techniques.Q Artificial intelligence Artificial intelligence is 'the theory and development of computer systems able to perform tasks normally ...


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In the book Artificial Intelligence: A Modern Approach (section 5.7, p. 185), Russell and Norvig write In 1965, the Russian mathematician Alexander Kronrod called chess "the Drosophila of artificial intelligence." John McCarthy disagrees: whereas geneticists use fruit flies to make discoveries that apply to biology more broadly, AI has used chess ...


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The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s (e.g. formal logic, automata, robots). Although the Turing test was proposed in the 1950s by Alan Turing, the work culminated back in the 1940s in the invention of the programmable digital computers, an abstract essence ...


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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 ...


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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.


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Many terms have 'mostly' the same meanings, and so the differences are just in emphasis, perspective, or historical descent. People disagree as to which label refers to the superset or the subset; there are people who will call AI a branch of ML and people who will call ML a branch of AI. I typically hear Machine Learning used as a form of 'applied ...


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Machine learning is a subfield of artificial intelligence, as the following diagram (taken from this blog post) illustrates.


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The hot topics of today might be the cold, wet ashes of tomorrow. For instance, the convergence speed of CNN and LSTM approaches, especially in combination, have diverted considerable attention away from basic RNN designs. Similarly, the cold topics of today might be the burning embers of tomorrow. Of course, some of the cold topics will stay cold. The ...


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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 ...


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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 ...


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The current, cutting edge AI methods all heavily rely on statistical modeling. You might want to browse the Data Science and Cross Validated stacks to see what people are doing, and the types of maths they are using. (This is not really my field, so I'll leave it to the Neural Network and Deep Learning crowd to provide more detail here.) I'd also strongly ...


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Over the years, many people attempted to define artificial intelligence. A lot of those definitions are summed up by Stuart Russell and Peter Norvig in their book Artificial Intelligence - A Modern Approach The definitions of AI can be summarised as falling into the following categories: Those that address thought process and reasoning (how an AI ...


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Why is Game Playing R&D a Focus of Resource Allocation? When examining the apparent obsession with game playing as researchers attempt to simulate portions of human problem solving abilities, the orthodoxy of the views of John McCarthy (1927 – 2011) may be misleading. Publication editorial bias and popular science fiction themes may obscure the ...


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The most important skill you need is self-discipline. Regarding the mathematical prerequisites, you will need to study statistics, probability theory, calculus, and linear algebra, given that e.g. most machine learning algorithms are highly based on concepts from these areas. Regarding the programming prerequisites, Python and R are usually a good choice, ...


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Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart" and Machine Learning is a current application of AI based around the idea that we should ...


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In simple words, Artificial intelligence is a field of science that is trying to mimic humans or other animals behavior. Machine Learning is one of the key tool/technology behind Artificial intelligence.


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Artificial Intelligence is a very broad field and therefore things will change accordingly. Some Prerequisites: (Being a student of CS you should have fulfilled them) Sound knowledge of algorithms and Data Structures. This skill will come in handy while solving problems that require use of alpha-beta pruning, minimax algorithm, etc. Basic knowledge of ...


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Well, there're certainly a lot of areas where you can contribute in research. Since you're saying you did a Master Thesis in deep Generative models, I assume you're comfortable in Machine and Deep Learning. Digital Epidemiology is one of the areas where you can certainly apply deep learning. It's still a relatively new field compared to other branches of ...


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Near solution to your problem definition is reinforcement learning. You can define some reward using the objective function and define some possible state space for the machine and finally solve the problem by reinforcement learning techniques (near to trial and error by learning the preferences).


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The book Machine Learning (1997) by Tom Mitchell covers case-based reasoning (CBR), a form of instance-based learning (nearest neighbor is the typical example of IBL) in chapter 8 (p. 230). T. Mitchell writes Instance-based methods such as $k$-NEAREST NEIGHBOR and locally weighted regression share three key properties. First, they are lazy learning methods ...


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I think any learning algorithm probably uses trial and error and analysis of the results with the ultimate goal of maximizing utility. It seems that the recent milestones in AI fall under the general umbrella of machine learning, which includes all forms of reinforcement learning. Essentially, any learning algorithm is using some form of statistical ...


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What is artificial intelligence? This question is ambiguous. I will address the two less ambiguous but related questions. What is the goal of the AI field? What is an artificial intelligence? What is the goal of the AI field? In the article What is artificial intelligence? (2007), John McCarthy, one of the founders of artificial intelligence and who also ...


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I find the statement troubling as the first confirmed algorithmic intelligence may have been a NIM automata, so from my perspective, the development of Algorithmic Intelligence is inseparable from combinatorial games. it would also seem that McCarthy does not hold the opinion that games are useful, which leads me to suspect he has never seriously studied the ...


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"artificial intelligence" is now a mature field and there are many subfields in it. there are entire theories erected around each subfield of artificial intelligence. a simple analogy would like "what are the skills needed for succeeding in mathematics/physics?". the answer really depends on what branch you want to delve into. if you are planning to go ...


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The OP asked about AI, not Machine Learning. Machine Learning is a sub-discipline of AI. Most AI used in games and motion planning does not involve machine learning. The background you will need to "jump into the field of AI": Math through calculus Algorithms Basic combinatorics and probability theory If you already have these requirements, I suggest ...


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You can start with they videos on Youtube. It is simple, with a basic knowledge in Python, you can do much things with ML. Machine Learning - Recipes #1 I found too, a new video of Cloud AI Adventures explain "The 7 Steps of Machine Learning", you can watch HERE. About math, I asked the same question when I started learning ML / AI. I'm not good with ...


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