"Basic mathematics: Start by reviewing the basics of mathematics, such as arithmetic, fractions, decimals, and percentages. If you feel that you are comfortable with these concepts, move on to more advanced topics such as algebra and geometry.
Algebra: Learn algebraic concepts such as equations, inequalities, polynomials, and factoring. You should be able to simplify expressions and solve equations, and be familiar with basic algebraic properties.
Geometry: Learn geometry concepts such as points, lines, angles, and shapes. You should be able to calculate areas and volumes, and be familiar with trigonometry.
Trigonometry: Learn the trigonometric functions such as sine, cosine, and tangent. You should also learn about their inverses, as well as how to use them to solve problems involving triangles and other geometric shapes.
Calculus: Start with differential calculus, which includes topics such as limits, derivatives, and optimization. Move on to integral calculus, which includes topics such as integrals and area under curves. This will be necessary for more advanced topics in robotics, AI, and machine learning.
Linear algebra: Learn the basics of linear algebra, including matrices, vectors, and systems of equations. You should be able to perform operations on matrices and vectors, and understand the concepts of determinants, eigenvalues, and eigenvectors.
Probability and statistics: Learn about probability distributions, expected values, and standard deviations. Understand the basics of statistical inference, such as hypothesis testing and confidence intervals.
Optimization: Learn about optimization techniques, such as gradient descent, and how to use them to optimize machine learning models.
Differential equations: Learn about differential equations, which are important for modeling the dynamics of robotic systems. This includes topics such as first-order and second-order differential equations.
Graph theory: Learn about graph theory and algorithms such as Dijkstra's algorithm and the minimum spanning tree algorithm. This is important for modeling the connectivity of networks of sensors, robots, and other components.
Information theory: Learn about basic concepts of information theory, including entropy, mutual information, and the Kullback-Leibler divergence.
Advanced topics: Once you have a solid foundation in the above areas, you can move on to more advanced topics such as control theory, functional analysis, and topology.
The roadmap I provided is a general guide to the topics that are important in robotics, AI, and machine learning, and the order in which they are presented is a reasonable progression from the basics to more advanced topics. However, the order in which you tackle these topics may depend on your background and personal interests.
For example, if you are already familiar with basic mathematics and have some programming experience, you may want to start with linear algebra and probability and statistics before moving on to calculus. On the other hand, if you have a strong background in calculus and physics, you may want to start with differential equations and optimization.
It is important to note that all of these topics are interconnected, so you may find that you need to revisit earlier topics as you progress through more advanced material. Ultimately, the key is to find a learning path that works best for you and helps you build a strong foundation in the mathematics that are relevant to robotics, AI, and machine learning."
This is the roadmap ChatGPT gave me. Would you I should follow this? I'm a bit confused since I was under the impression that I should deal with linear algebra and Probability and statistic before calculus but I'm not sure.