In our organization, there are many people who are into analytics and data science who are OK in their work with in the sense that they know what packages in R/Python etc to use, what algorithms to call for a particular type of problem etc. The problem is that they have very little knowledge of underlying mathematics and are not able to think beyond calling ready made functions i.e. they can never build a custom solution to specific problem. Let us call them as Function Callers as opposed a genuine Data Scientist who has a fair knowledge of the underlying mathematics.
We want to conduction a training course to teach the mathematical foundations of machine learning to the Function Callers and help them become Data Scientists. For this we have shortlisted some ten topics namely.
• Probability • Various Probability distributions and data • Maximum likelihood methods • Linear Algebra (advanced) • Single variable calculus, Multi variable calculus • Vector calculus • Graphical models • Bayesian networks • Optimization techniques • Statistical models
This is going to be an 80 hour course so it is not possible to cover everything in detail like a college/university course.
Question: Assume that the course will have enough time to teach and implement only 3 or 4 things in each of the topics mentioned above. So given this background, what are the best 3 or 4 things that can be covered under each topic?
Note: The target audience have either science or college level mathematics background but they do not have deep knowledge about the mathematics that is used in machine learning.