About the Course
The core concept of the course is random variable — i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables
Course Objectives
01
Explain notions of conditional probability and independence of events, describe Bernoulli scheme and understand the law of total probability and Bayes’s rule
02
Calculate expected value, variance, probability distribution and probability mass function
03
Understand notion of continuous random variable, PDF, CDF, independence, covariance, correlation
Learning Outcomes
![](/pubs/share/direct/799521394.jpg)
1. Probabilities
![](/pubs/share/direct/799521457.jpg)
2. Sampling
![](/pubs/share/direct/799521463.jpg)
3. Data analysis
![](/pubs/share/direct/799521480.jpg)
4. Data visualization
Course Syllabus
Week 1. Conditional probability and Independence
Week 2. Random variables
Week 3. Systems of random variables; properties of expectation and variance, covariance and correlation
Week 4. Continuous random variables
Week 5. From random variables to statistical data. Data summarization and descriptive statistics
Week 6. Correlations and visualizations
Teacher
Ilya V. Schurov
Associate Professor
Learning Activities
Lectures
Online
Low-Stakes Assignments
Tests
High-Stakes Assignments
Final project
Cost and Conditions
17 000 ₽
Full access to the learning materials + Graduation document
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