The Mathematics of Machine Learning for R&D heavy roles
Mathematics and Machine Learning are closely intertwined, with both disciplines striving to use data to make better decisions.
There are numerous reasons why the mathematics of Machine Learning is so important for the success of ML models and engineering automated systems.
Mathematics is an essential part of machine learning because it provides the tools and techniques needed to develop algorithms that can learn from data. It is used to create models that can accurately predict outcomes, identify patterns, and make decisions. It also helps to understand the underlying principles of machine learning and how different algorithms work.
To become successful in ML and AI, it's important to understand the underlying mathematical concepts that are often obscured by the hype of marketing and inadequate roadmaps. This guide provides clear explanations and additional resources to give you a mathematically sound introduction to key topics, helping you gain the knowledge you need to learn Machine Learning and AI.
If you're intimidated by maths, we recommend Mathematical Thinking by Terence Tao (masterclass by world-renowned mathematician) for staying motivated.
Math for Machine Learning, Deep Learning and AI
Clearly, Mathematics is important in machine learning. As such, this post aims to highlight important topics one must cover to become highly equipped for R&D heavy roles.
Linear algebra is an essential topic in mathematics that is necessary for comprehending the underlying structure of machine learning algorithms. It encompasses concepts such as vectors, matrices, and linear transformations.
Linear algebra is an integral part of machine learning, as it is used to structure data and carry out operations on it. It is also used to solve linear equations, which are essential for many ML algorithms. Moreover, linear algebra is employed to calculate gradients and optimize parameters in neural networks.
It is critical for machine learning since it provides the mathematical basis for understanding and manipulating data. Basic topics for novices include vectors, matrices, linear transformations, and eigenvalues and eigenvectors.
Linear algebra can be difficult to learn. Here are some of the best resources that cover matrix theory and linear algebra, with a focus on topics related to machine learning.
- Linear Algebra Courses for Machine Learning
- Mathematics for Machine Learning: Linear Algebra
- Linear Algebra for Machine Learning (Python)
Calculus is an essential part of machine learning, as it provides the mathematical foundations for understanding and optimizing complex models. It is used to understand how data changes over time and to calculate derivatives and integrals, which can be useful for topics such as optimization, probability, and statistics. It can be exceedinly useful to understand the behavior of functions and how they can be applied to model real-world phenomena.
Having a basic understanding of calculus is not necessary for using machine learning, but it can be beneficial in order to gain a deeper understanding of the mathematics behind algorithms and models. Learning calculus can help you read and comprehend equations more accurately, allowing you to speak precisely about the properties of functions and better understand their behavior. In short, calculus is a powerful tool that can help you describe and understand the progress of machine learning.
For those looking to learn calculus in order to improve their understanding of machine learning, some essential topics include differentiation, vector calculus and optimization. Here are some great resources for getting started.
Probability and Statistics
Probability and Statistics are fundamental to Machine Learning. Having a good grasp of the underlying mathematics behind ML algorithms is essential for optimizing models. To this end, it is important to understand basic concepts such as probability distributions, Bayesian inference, and linear regression.
These concepts provide the mathematical foundations for understanding and predicting the behavior of complex systems. They are used to identify patterns in data, build models, and make predictions. Without a strong understanding of probability distributions, hypothesis testing, and Bayesian inference it would be difficult to develop effective machine learning systems.
If you are looking for resources to get started with Probability and Statistics, here are a few options.
- Basic Statistics Concepts
- Probability and Statistics Courses (Non-programming)
- Computational Statistics with Python Courses
- Statistics with R Courses
Mathematical optimization is used to identify the optimal parameters for a given model and to minimize the cost associated with it. Commonly used optimization techniques in machine learning include gradient descent, stochastic gradient descent, and Newton's method.
Optimization is a powerful tool for machine learning. It is beneficial for ML researchers to become familiar with gradient descent, simulated annealing, and genetic algorithms as these techniques can help improve the accuracy and performance of their models.
Numerical Analysis is used in machine learning to optimize algorithms and solve problems related to optimization. It involves the use of mathematical models and numerical methods to analyze data and make predictions.
By understanding the basics of numerical analysis and its applications in machine learning, beginners can gain a better understanding of how to apply machine learning algorithms in their projects. This knowledge will help you develop more accurate models that can be used for predictive analytics or other tasks.
Graph theory is a powerful tool for feature selection and dimensionality reduction in machine learning. Graphs can be used to represent relationships between features, allowing us to identify which features are most important and which can be removed or combined. Graphs can also be used to identify clusters of related features, allowing us to reduce the number of dimensions in our data set. It is helpful to identify patterns in data that may not be obvious when looking at the raw data alone.
Graph Theory is powerful tool for understanding the structure of data and even how to use it to create more accurate models. Fundamental topics such as graph traversal algorithms, graph search algorithms, shortest path algorithms, network flow algorithms and graph clustering algorithms provide an insight into how graphs are used in machine learning applications and how they can be leveraged for predictive modeling.
Algorithms are the foundation of Machine Learning (ML) and Artificial Intelligence (AI). They provide instructions for machines to learn from data, allowing them to make predictions and decisions. Common algorithms used in ML include linear regression, logistic regression, decision trees, and support vector machines.
In addition to algorithms, there are core concepts that are essential for ML researchers to understand. These include supervised learning (training models on labeled data to predict outcomes), unsupervised learning (clustering data without labels), reinforcement learning (using rewards/punishments to solve problems), and deep learning (using artificial neural networks).
Having a good understanding of how algorithms work is important for ML engineers as it helps them identify the best algorithm for a given task and optimize models.
Mathematics provides the foundation for understanding the underlying principles of Machine Learning, allowing us to develop more accurate and efficient models. Without a strong grasp of mathematical concepts, it is difficult to fully comprehend the complexities of ML algorithms and their applications.
If you want to learn machine learning, you’ll have to think on your feet. The concepts we explained in this guide can be difficult to learn for someone only familiar with Arithmetic. Understanding these concepts is essential to learning the applications of Machine Learning to secure an entry-level job.
These resources can help strengthen your ML skills.
We hope your journeys will go as you hope, and that the resources listed in this article will equip you for Mathematical Thinking.
- OCW MIT: https://ocw.mit.edu/courses/18-657-mathematics-of-machine-learning-fall-2015/
- Cambridge University Press: https://mml-book.github.io/
Disclosure: The views expressed in this article are those of the author and do not reflect the views of Datacamp, Coursera, edX or their partners. This article may contain links to content on third-party sites. By providing such links, kanger.dev does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.
kanger.dev is supported by our audience. We may earn affiliate commissions from buying links on this site.