Mathematics for Machine Learning and Data Science Specialization

课程 1：Linear Algebra for ML & DS

1. Systems of linear equations

   • 二元/三元线性方程组

   • 奇异矩阵、非奇异矩阵、行列式

   • numpy

2. Solving systems of linear equations

   • 消元法、矩阵行化简、阶梯形矩阵、矩阵秩

3. Vectors and Linear Transformations

   • 向量范数、点积、矩阵乘法、矩阵逆、神经网络与矩阵运算

4. Determinants and Eigenvectors

   • 行列式性质、特征值与特征向量、PCA 应用

课程 2：Calculus for ML & DS

• Derivatives and Optimization

• Gradients and Gradient Descent

• Optimization in Neural Networks and Newton’s Method

课程 3：Probability & Statistics for ML & DS

• Introduction to Probability and Probability Distributions

• Describing probability distributions and probability distributions with multiple variables

• Sampling and Point estimation

• Confidence Intervals and Hypothesis testing