# Course details

Linear Algebra & Probability: Advanced Linear Algebra

### Linear Algebra & Probability: Advanced Linear Algebra

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

Discover how to apply advanced linear algebra and its principles to derive machine learning implementations. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. The role of statistics and probability, with focus on parameter estimation and Gaussian distribution, is also covered.

Expected Duration (hours)
1.7

Lesson Objectives

Linear Algebra & Probability: Advanced Linear Algebra

• discover the key concepts covered in this course
• use Python libraries to implement principal component analysis with matrix multiplication
• describe sparse matrix and the operations that can be performed on sparse matrix
• define the concept of tensors in linear algebra and list the arithmetic operations that can be applied on tensors
• implement Hadamard product on tensors using Python
• describe singular-value decomposition and how to calculate it
• reconstruct a rectangular matrix from single-value decomposition
• recognize the characteristics of probability that are applicable in machine learning
• describe probability in linear algebra and its role in machine learning
• recall the types of random variables and the functions that can be used to manage random numbers in probability
• describe the concept and characteristics of central limit theorem and means and recognize common usage scenarios
• describe parameter estimation and distribution using Gaussian
• describe binomial distribution and its characteristics
• recall the arithmetic operations that can be applied on tensors, list the features of multivariate statistics that are applicable in machine learning, and implement Hadamard product on tensors using Python
• Course Number:
it_mllapbdj_02_enus

Expertise Level
Intermediate