What kind of knowledge is required to jump into the field of AI? What mathematics is required? How good I should be in mathematics?
Currently, I have just started programming. I would be grateful if you suggest me reading material.
To excel in in AI you need a mathematical intuition or point of view. In order to become a full stack AI engineer, it is important that you have a firm understanding of the mathematical foundations of machine learning.
My advice to anyone preparing to jump into the field is that learning mathematics is about doing. Remember the 20/80 rule. You need to study theory 20% of the time and practice what you have learnt 80% of the time in order to be proficient.
The first step before you dive into undergraduate math is for you to refresh on foundation level math. This includes revisiting and mastering high school mathematics especially Algebra. This is necessary for you to understand higher courses.
Matrix Operations, Projections, Eigenvalues & Eigenvectors, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigen-decomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning.
A nice thing about Linear Algebra is that there are wonderful online resources such as Khan Academy's course on linear algebra https://www.khanacademy.org/math/linear-algebra.
Probability Rules & Axioms, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Bayes' Theorem, Random Variables, Variance and Expectation, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.
I refer you to this online Statistics and Probability MOOC at Khan Academy https://www.khanacademy.org/math/probability
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.
For in depth tuition, I recommend that you sign up for Andrew Ng's Coursera course available here https://www.coursera.org/learn/machine-learning.
Topics such as Differential and Integral Calculus, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution, Partial Derivatives, Vector-Values Functions. Here is a lint to Khan Academy's Calculus course https://www.khanacademy.org/math/calculus-home.
This consists of math topics that are not covered in the above four major areas. They include Information Theory (Entropy, Information Gain), Function Spaces and Manifolds, Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits, Cauchy Kernel, Fourier Transforms).
Below is an Excellent resource of free Machine Learning Mathematics ebooks http://blog.paralleldots.com/data-science/list-of-free-must-read-books-for-machine-learning/
Finally to keep up with recent development and the latest papers, I recommend that you follow this blog, which aggregates AI and ML papers, http://www.arxiv-sanity.com/.
The current, cutting edge AI methods all heavily rely on statistical modeling. You might want to browse the Data Science and Cross Validated stacks to see what people are doing, and the types of maths they are using. (This is not really my field, so I'll leave it to the Neural Network and Deep Learning crowd to provide more detail here.)
Game Theory is also important, because the other core issues of optimal decisio making involve imperfect information and incomplete information. (The former is seen more in combinatorial games, but both lead to the need for probability analysis.)
Jumping into neural networks isn't that complicated.
NEURAL NETWORKS DEMYSTIFIED 1: Classification Problems helped me get started.
The OP asked about AI, not Machine Learning. Machine Learning is a sub-discipline of AI. Most AI used in games and motion planning does not involve machine learning.
The background you will need to "jump into the field of AI":
If you already have these requirements, I suggest looking at different types of AI to see what sounds most interesting to you. Mostly, getting into AI just means learning how the algorithms work. The algorithms for facial recognition (a machine learning problem) for example are much different than coordinating a team of robots (a combinatorial optimization problem).
You can start with they videos on Youtube. It is simple, with a basic knowledge in Python, you can do much things with ML.
I found too, a new video of Cloud AI Adventures explain "The 7 Steps of Machine Learning", you can watch HERE.
About math, I asked the same question when I started learning ML / AI. I'm not good with math, just the basics (also developer). But today, with TensorFlow and other libraries, you can focus on the theory and definition. So, do not worry, over time, AI models will be construct with a few buttons and labels ... maybe it's happening.