Mathematics Skills for AI and Machine Learning
Mathematics Skills for Ai And Machine Learning: The Foundation
There is a lot what usually was a virtue of a human being that a machine can do now. From facial recognition to delivering the products, the revolutionary Artificial Intelligence is taking strides and getting indulged with many of the common repetitive tasks and freeing humans for doing something lot more advanced intellectually. The big question is, how did the machines manage to get this “smart” and how do they make sense of all these activities that they perform and get better with their performance as well.
Let’s delve into past, we are talking about “Thinking Machines” of the 1950s, 60s and 70s when the scientists were of the view that a machine can walk, talk and behave like a human being. But before doing any of such things, the machine had to “learn” it and that was the biggest challenge. The debate kept going on and yet came the AI winters in the 1980s when all the discussions and advancements on building a “Thinking Machines” were halted at once 1.
Mathematics Skills for Ai And Machine Learning: The Breakthrough
However, in the 1990s, scientists like Judea Pearl found a breakthrough in the wake of statistics and mathematics. There is a way of making the machines learn, though not that much but a trait can be developed and then the capability can be further enhanced. But for that, lots and lots of data is required which was then assumed as the biggest hindrance 2.
However, the revolutionary solution to this was the internet and boy was it then utilized. The data started pouring in, but not in a quantity required to make high-level predictions. Therefore, the whole project kept lurking and after the invention of the smartphone, an explosion of data occurred leading to the Machine Learning enthusiast utilizing it.
So, what made it all possible to make all these predictions and receive a courier from a robot? Machine learning that is and how exactly Machines managed to learn? Bing! Mathematics and Statistics. It is all about making sense of the data and then uses the tools to bring it to clinical form for further trails.
Mathematics Skills for Ai And Machine Learning: The Role of Mathematics
The most important question in this context is that what level of mathematics skills are to be acquired to work on Machine Learning technology. One other important factor is the ongoing research regarding the mathematical theories and formulations for machine learning 3. There are various mathematical skills that are required for a machine learning engineer and this distribution goes as follows:
- Linear Algebra, 35%
- Probability 25%
- Multivariate Calculus, 15%
- Vectors, 15%
- Analytic Geometry, 10%
This particular mix represents the importance of the core mathematical skills for a machine learning engineer. Now let us look at some of the most important mathematical concepts that play a vital role in machine learning
Mathematics Skills for Ai And Machine Learning: Linear Algebra
It is considered that linear algebra is actually the mathematics of the twenty-first century and is seen as the back bone of the whole machine learning process. Linear Algebra basically is a systematic process to represent the linear equations. Furthermore, it is also considered as a systematic representation of the knowledge for convenient understanding for the computer to understand it and the key operations of linear algebra refer to lot more systematic rules 4.
Mathematics Skills for Ai And Machine Learning: Multivariate calculus
Multivariate calculus is primarily used for mathematical optimization of a given function and is also known as partial differentiation. Furthermore, this technique includes integration and differentiation of functions with more than one variable and is primarily deployed during feature engineering which is considered the most sensitive part of the whole machine learning process 5. Thus, for the machines learning engineers, good solid command on these mathematics skills for ai and machine learning is extremely important.
Mathematics Skills for Ai And Machine Learning: Probability
None the less, probability holds the whole machine learning projects together and anything when goes wrong with its application, the whole model suffers. Primarily used through various types of distributions like Gaussian distribution and probability density functions, the main aim of using the probability function is to test the hypothesis.
In machine learning, the probability function is the key to quantifying things as the machine learning systems are working with a lot of data to find patterns among the data. Sometimes, the mere logic doesn’t work and a grey area of uncertainty arises. The more is the uncertainty, the more will be the relevance of the probability function 6.
Mathematics Skills for Ai And Machine Learning: Vectors
Vectors are simply very important foundational elements of the linear algebra. Vectors become most important when the models to be deployed such as fitting the target variable (y) for the training of the model. A vector includes a number scalars and is called a tuple 7.
Also considered as a line specifically from the origin of the space of the relevant vector. None the less, vectors are considered the most important analogy in machine learning in the context of vectors-as-coordinates.
Mathematics Skills for Ai And Machine Learning: Analytic Geometry
This is one of the most sought functions in mathematics when deployed din machine learning and all because of its symbolism to denote various that reflect on various functions of mathematics. The machine learning engineers use this function to denote their aspirations and find the guidance logs in order to keep track of the functions they have deployed 8. This then helps them in determining the flow of the functions they have deployed and later interpret the results to take further important steps.
Mathematics Skills for Ai And Machine Learning: The Conclusion
There is various other function of mathematics that are utilized in machine learning and are considered as the foundation on which the machine learning models dwell and finally a solution to our major problems are identified. Not only in retail & production but in the medical technology the mathematics has made it possible for machine learning engineers to bring about the best of their work and utilize their skills to the maximum extent for the betterment of human beings and the society itself. Stay tuned for my next article, and don’t forget to leave your insight in the comments section too. See ya, cheers!
Bibliography:
- Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53-53.
- Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
- Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
- Poggio, T., & Smale, S. (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50(5), 537-544.
- Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
- DasGupta, A. (2011). Probability for statistics and machine learning: fundamentals and advanced topics. Springer Science & Business Media.
- Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of machine learning research, 2(Nov), 45-66.
- Qiu, J. Q., & Zhang, M. (2006, August). Fuzzy space analytic geometry. In 2006 International Conference on Machine Learning and Cybernetics (pp. 1751-1755). IEEE.
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