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Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Statistical Inference

Statistical Inference

About this course: Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Created by:   Johns Hopkins University
  • Brian Caffo, PhD
    Taught by:    Brian Caffo, PhD, Professor, Biostatistics
    Bloomberg School of Public Health
  • Roger D. Peng, PhD
    Taught by:    Roger D. Peng, PhD, Associate Professor, Biostatistics
    Bloomberg School of Public Health

  • Jeff Leek, PhD
    Taught by:    Jeff Leek, PhD, Associate Professor, Biostatistics
    Bloomberg School of Public Health
Basic Info
Course 6 of 10 in the Data Science Specialization.
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.0See what learners said
Syllabus
WEEK 1
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.  

10 videos11 readings
  1. Video: Introductory video
  2. Reading: Welcome to Statistical Inference
  3. Reading: Some introductory comments
  4. Reading: Pre-Course Survey
  5. Reading: Syllabus
  6. Reading: Course Book: Statistical Inference for Data Science
  7. Reading: Data Science Specialization Community Site
  8. Reading: Homework Problems
  9. Reading: Probability
  10. Video: 02 01 Introduction to probability
  11. Video: 02 02 Probability mass functions
  12. Video: 02 03 Probability density functions
  13. Reading: Conditional probability
  14. Video: 03 01 Conditional Probability
  15. Video: 03 02 Bayes' rule
  16. Video: 03 03 Independence
  17. Reading: Expected values
  18. Video: 04 01 Expected values
  19. Video: 04 02 Expected values, simple examples
  20. Video: 04 03 Expected values for PDFs
  21. Reading: Practical R Exercises in swirl 1
  22. Ungraded Programming: swirl Lesson 1: Introduction
  23. Ungraded Programming: swirl Lesson 2: Probability1
  24. Ungraded Programming: swirl Lesson 3: Probability2
  25. Ungraded Programming: swirl Lesson 4: ConditionalProbability
  26. Ungraded Programming: swirl Lesson 5: Expectations
Graded: Quiz 1
WEEK 2
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals. 

10 videos4 readings
  1. Reading: Variability
  2. Video: 05 01 Introduction to variability
  3. Video: 05 02 Variance simulation examples
  4. Video: 05 03 Standard error of the mean
  5. Video: 05 04 Variance data example
  6. Reading: Distributions
  7. Video: 06 01 Binomial distrubtion
  8. Video: 06 02 Normal distribution
  9. Video: 06 03 Poisson
  10. Reading: Asymptotics
  11. Video: 07 01 Asymptotics and LLN
  12. Video: 07 02 Asymptotics and the CLT
  13. Video: 07 03 Asymptotics and confidence intervals
  14. Reading: Practical R Exercises in swirl Part 2
  15. Ungraded Programming: swirl Lesson 1: Variance
  16. Ungraded Programming: swirl Lesson 2: CommonDistros
  17. Ungraded Programming: swirl Lesson 3: Asymptotics
Graded: Quiz 2
WEEK 3
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson. 

11 videos5 readings
  1. Reading: Confidence intervals
  2. Video: 08 01 T confidence intervals
  3. Video: 08 02 T confidence intervals example
  4. Video: 08 03 Independent group T intervals
  5. Video: 08 04 A note on unequal variance
  6. Reading: Hypothesis testing
  7. Video: 09 01 Hypothesis testing
  8. Video: 09 02 Example of choosing a rejection region
  9. Video: 09 03 T tests
  10. Video: 09 04 Two group testing
  11. Reading: P-values
  12. Video: 10 01 Pvalues
  13. Video: 10 02 Pvalue further examples
  14. Reading: Knitr
  15. Video: Just enough knitr to do the project
  16. Reading: Practical R Exercises in swirl Part 3
  17. Ungraded Programming: swirl Lesson 1: T Confidence Intervals
  18. Ungraded Programming: swirl Lesson 2: Hypothesis Testing
  19. Ungraded Programming: swirl Lesson 3: P Values
Graded: Quiz 3
WEEK 4
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests. 

9 videos4 readings
  1. Reading: Power
  2. Video: 11 01 Power
  3. Video: 11 02 Calculating Power
  4. Video: 11 03 Notes on power
  5. Video: 11 04 T test power
  6. Video: 12 01 Multiple Comparisons
  7. Reading: Resampling
  8. Video: 13 01 Bootstrapping
  9. Video: 13 02 Bootstrapping example
  10. Video: 13 03 Notes on the bootstrap
  11. Video: 13 04 Permutation tests
  12. Reading: Practical R Exercises in swirl Part 4
  13. Ungraded Programming: swirl Lesson 1: Power
  14. Ungraded Programming: swirl Lesson 2: Multiple Testing
  15. Ungraded Programming: swirl Lesson 3: Resampling
  16. Reading: Post-Course Survey
Graded: Quiz 4
Graded: Statistical Inference Course Project
How It Works
Coursework
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
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.


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