Guest blog post by Wale Akinfaderin, PhD Candidate in Physics. 

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learnWekaTensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

Why Worry About The Maths?

There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below:

1. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

2. Choosing parameter settings and validation strategies.

3. Identifying underfitting and overfitting by understanding the Bias-Variance tradeoff.

4. Estimating the right confidence interval and uncertainty.

What Level of Maths Do You Need?

The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques. The answer to this question is multidimensional and depends on the level and interest of the individual. Research in mathematical formulations and theoretical advancement of Machine Learning is ongoing and some researchers are working on more advance techniques. I'll state what I believe to be the minimum level of mathematics needed to be a Machine Learning Scientist/Engineer and the importance of each mathematical concept.

1. Linear Algebra: A colleague, Skyler Speakman, recently said that "Linear Algebra is the mathematics of the 21st century" and I totally agree with the statement. In ML, Linear Algebra comes up everywhere. Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning. The amazing thing about Linear Algebra is that there are so many online resources. I have always said that the traditional classroom is dying because of the vast amount of resources available on the internet. My favorite Linear Algebra course is the one offered by MIT Courseware (Prof. Gilbert Strang).

2. Probability Theory and Statistics: Machine Learning and Statistics aren't very different fields. Actually, someone recently defined Machine Learning as 'doing statistics on a Mac'. Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes' Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

3. Multivariate Calculus: Some of the necessary topics include Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

4. Algorithms and Complex Optimizations: This is important for understanding the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting sparsity in our datasets. Knowledge of data structures (Binary Trees, Hashing, Heap, Stack etc), Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods are needed.

5. Others: This comprises of other Math topics not covered in the four major areas described above. They include Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Some online MOOCs and materials for studying some of the Mathematics topics needed for Machine Learning are:

Finally, the main aim of this blog post is to give a well-intentioned advice about the importance of Mathematics in Machine Learning and the necessary topics and useful resources for a mastery of these topics. However, some Machine Learning enthusiasts are novice in Maths and will probably find this post disheartening (seriously, this is not my aim). For beginners, you don't need a lot of Mathematics to start doing Machine Learning. The fundamental prerequisite is data analysis as described in this blog post and you can learn the maths on the go as you master more techniques and algorithms.

Note from the Editor: I believe that it is possible to design Machine Learning algorithms that are math-free. Most of my algorithms (including my most recent one) are math-free.

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Comment by Vikrant Choudhary on November 29, 2018 at 11:02pm

thanks you for this ... very useful indeed.

Comment by Javier Linkolk on September 3, 2018 at 3:14pm

Excellent. Thank you for this information very valuable.

Comment by TRENTON John MCKINNEY on April 21, 2018 at 8:45am

It should be noted, the image of the cost function is from the Machine Learning class on Coursera taught by Andrew Ng.

Comment by Neeraj Sharma on January 12, 2018 at 7:39am

Very good post and appropriate example. Because he has explained each and every slide in a very simple way. I have gone through whole course and didn't felt anything which was difficult to understand. Actually no one really need higher degree in maths, its attitude which drives and you have presented it very nicely.

Comment by Daryl Heinz on July 14, 2017 at 3:44am
There are individuals, elitists, who want to limit access to data science as a career by perpetuating the "fud" (fear uncertainty and doubt) that a PhD degree is necessary to be a data scientist. In reality anyone with the appropriate attitude and a focused approach to the maths you mention can indeed retrain themselves to become a functional data scientist. Thank you for shattering the "glass ceiling of elitism" that has been perpetuated by ignorant and insecure and vocal individuals.
Comment by Maria Ane Dias on February 20, 2017 at 5:15pm

I love your post! I'm a Software Engineer, I'm studying math and discovering this new world of ML. There's a long road, and this post and articles will be very helpful!

Comment by David Reinke on February 16, 2017 at 12:08pm

I pretty much agree, although I'd put a bit more emphasis on real analysis and information theory -- at the level of Rudin's book for the former, and Cover & Thomas for the latter -- especially if one gets into MDL. I also recommend exploring MIT's Open Courseware site (https://ocw.mit.edu/index.htm); there are good courses in the math department, but electrical engineering is probably the best "go to" for machine learning; also other departments; just to name a few, economics, management, brain & cognitive science, engineering systems division, and health sciences and technology all offer courses that deal with some aspects of machine learning. One bit of advice: almost all MIT courses other than group labs are centered around problem sets; do those successfully and you have mastered the course.

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