Об этом курсе: Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Для кого этот курс: This class is for students who already have had a class in regression modeling and are familiar with the area who would like to see a more advanced treatment of the topic.
Автор: Johns Hopkins University
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Уровень | Продвинутые функции |
| Выполнение | 6 weeks of study, 1-2 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Background
We cover some basic matrix algebra results that we will need throughout the class. This includes some basic vector derivatives. In addition, we cover some some basic uses of matrices to create summary statistics from data. This includes calculating and subtracting means from observations (centering) as well as calculating the variance.
7 видео, 4 материала для самостоятельного изучения
- Reading: Welcome to the class
- Reading: Course textbook
- Reading: Grading
- Reading: In this module
- Видео: Matrix derivatives
- Видео: Coding example
- Видео: Centering by matrix multiplication
- Видео: Coding example
- Видео: Variance via matrix multiplication
- Видео: Coding example
Оцениваемый: Background Quiz
НЕДЕЛЯ 2
One and two parameter regression
In this module, we cover the basics of regression through the origin and linear regression. Regression through the origin is an interesting case, as one can build up all of multivariate regression with it.
6 видео, 2 материала для самостоятельного изучения
- Reading: Before you begin
- Видео: Centering first
- Видео: Coding example
- Reading: Before you begin
- Видео: Connection with linear regression
- Видео: Coding example
- Видео: Fitted values and residuals
Оцениваемый: One Parameter Regression Quiz
НЕДЕЛЯ 3
Linear regression
In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships.
8 видео, 2 материала для самостоятельного изучения
- Reading: Before you begin
- Видео: Coding example
- Видео: Prediction
- Видео: Coding example
- Видео: Residuals
- Видео: Coding example
- Reading: Generalizations
- Видео: Generalizations
- Видео: Generalizations example
Оцениваемый: Linear Regression Quiz
НЕДЕЛЯ 4
General least squares
We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome.
6 видео, 1 материал для самостоятельного изучения
- Reading: Before you begin
- Видео: Coding example
- Видео: Second derivation of least squares
- Видео: Projections
- Видео: Third derivation of least squares
- Видео: Coding example
Оцениваемый: General Least Squares Quiz
НЕДЕЛЯ 5
Least squares examples
Here we give some canonical examples of linear models to relate them to techniques that you may already be using.
4 видео
- Видео: Group effects
- Видео: Change of parameterization
- Видео: ANCOVA
Оцениваемый: Least Squares Examples Quiz
НЕДЕЛЯ 6
Bases and residuals
Here we give a very useful kind of linear model, that is decomposing a signal into a basis expansion.
6 видео
- Видео: Bases 2, Fourier
- Видео: Bases 3, SVDs
- Видео: Bases, coding example
- Видео: Introduction to residuals
- Видео: Partitioning variability
Оцениваемый: Bases Quiz
Оцениваемый: Residuals Quiz
Часто задаваемые вопросы
Как это работает
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Авторы
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Рейтинги и отзывы
Very thorough and rigorous. A great review for me.
Great, detailed walk-through of least squares. Linear Algebra is a must for this course. To follow the last part requires knowledge of matrix (eigen?)decomposition, which derailed me somewhat.
Great Course
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
CL
Very good