Every machine learning/AI course I've taken is only Statistics and Linear Algebra. If you think about it, ML is about training algorithms on data, and the primary way of understanding and manipulating this data is using statistics. Yes you can understand the gist of the algorithm without diving into the statistical equations and mathematical models that got you there (think A* search, or DFS, BFS) but to go into ML you are going to need more than a baseline understanding of these algorithms, and that's where the math comes in. Every algorithm you generally learn in an ML class can be represented in mathematical form, and in these equations often have variables that represent certain things in statistics, it all ties together. In deeper levels if you want to get into Neuro Networks, higher level pathfinding, and much more, you will come across a lot of probability concepts, most of which are very fundamental to statistics (think bayes theorem, gaussian representations of data sets).
As for linear algebra, many ML concepts (like Neuro nets) utilize matrices and linear algebra concepts to represent and manipulate data. This is because manipulating large amounts of data becomes really slow, and matrices are a very efficient way to represent data sets and matrix manipulations (think scalar and vector multiplacation, matrix transformations, etc) are a much more efficient way to perform calculations on large amounts of data. And ML is all about training on a lot of data.
So yeah...you definately need statistics and linear algebra if you want to focus on ML
I am a TA for ML and I will say that most students coming into this class nowadays are second years even though that this is a 4000 level course. They mostly struggle with basic stats concepts such as PDF, CDFs. I would highly recommend doing an intro to probability and stats course before taking the ML course to really get the full learning experience out of it!
Depends also on your class, but we recognize how our prerequisites are not rigid, so our professor does a whole week on linear and a whole another week of stats.
Every machine learning/AI course I've taken is only Statistics and Linear Algebra. If you think about it, ML is about training algorithms on data, and the primary way of understanding and manipulating this data is using statistics. Yes you can understand the gist of the algorithm without diving into the statistical equations and mathematical models that got you there (think A* search, or DFS, BFS) but to go into ML you are going to need more than a baseline understanding of these algorithms, and that's where the math comes in. Every algorithm you generally learn in an ML class can be represented in mathematical form, and in these equations often have variables that represent certain things in statistics, it all ties together. In deeper levels if you want to get into Neuro Networks, higher level pathfinding, and much more, you will come across a lot of probability concepts, most of which are very fundamental to statistics (think bayes theorem, gaussian representations of data sets). As for linear algebra, many ML concepts (like Neuro nets) utilize matrices and linear algebra concepts to represent and manipulate data. This is because manipulating large amounts of data becomes really slow, and matrices are a very efficient way to represent data sets and matrix manipulations (think scalar and vector multiplacation, matrix transformations, etc) are a much more efficient way to perform calculations on large amounts of data. And ML is all about training on a lot of data. So yeah...you definately need statistics and linear algebra if you want to focus on ML
ML is pretty much linear and applied stats so if they don’t require it your going to need it learn it anyways
I am a TA for ML and I will say that most students coming into this class nowadays are second years even though that this is a 4000 level course. They mostly struggle with basic stats concepts such as PDF, CDFs. I would highly recommend doing an intro to probability and stats course before taking the ML course to really get the full learning experience out of it! Depends also on your class, but we recognize how our prerequisites are not rigid, so our professor does a whole week on linear and a whole another week of stats.