Introduction To Machine Learning Self-Evaluation Test
2017-03-28 09:27
337 查看
Preface
Section 1 - Mathematical background
Multivariate calculus
take derivatives and integrals;derive gradients of multivariate functions.
Linear algebra
multiply vector and matrices;matrix inversion;
eigenvectors;
eigenvalues ;
matrix factorization.
probability and statistics
mean and variance;common probability distribution : Gaussian and Uniform distribution;
conditional distribution and Bayes rule;
calculate the likelihood (probability)
deriving the parameters of the distribution
Section 2 - Usage
If pass "Modest Background Test" , you are good in shape of take the class.If pass "Minimum Background Test" , but not the "Modest Background Test", then you can take the class but you should expect to devote extra time to fill in necessary math background.
Necessary Minimum Background Test
Multivariate calculus
partial derivativeVectors and matrices
productinverse
rank
Probability and statistics
相关文章推荐
- Introduction to Machine Learning (一)
- Introduction to Machine Learning
- Introduction to Machine Learning
- A Gentle Introduction to Singular-Value Decomposition for Machine Learning
- Introduction to Machine Learning
- 【菜鸟学深度】Introduction to Machine Learning CMU-10701
- An introduction to machine learning with scikit-learn
- Introduction to machine learning
- Very Brief Introduction to Machine Learning for AI
- An introduction to machine learning with scikit-learn
- Introduction to Machine Learning
- Introduction to Machine Learning
- Andrew NG机器学习课程笔记系列之——Introduction to Machine Learning
- An introduction to machine learning with scikit-learn
- Introduction to Machine Learning
- Introduction to Machine Learning
- An Introduction to Machine Learning with Python
- Introduction.to.Machine.Learning.with.Python 笔记
- Quick Introduction to Boosting Algorithms in Machine Learning
- A Gentle Introduction to Applied Machine Learning as a Search Problem (译文)