Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data.
Data Science Decisions in Time: Using Data Effectively
This course is part of Data Science Decisions in Time Specialization
Recommended experience
What you'll learn
By the end of the course you will: (1) understand sequential testing and thus when to stop collecting data and (2) how this concept is used today.
Skills you'll gain
Details to know
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August 2024
5 quizzes, 6 assignments
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There are 5 modules in this course
This module introduces the class and the approach to teaching it to be used for the next five weeks. We begin with simple sequential data, similar to Wald’s model: data arrives from a distribution and is not time dependent. This can be generative data. We then explore increasingly complex data from distributions collected for health or business reasons. We finish the week with connections to code work and to AI.
What's included
5 videos2 readings1 quiz1 assignment1 discussion prompt
This module is the bridge into Markov Processes and Markov Chains. Thompson sampling is an old algorithm, that has been revived and is currently in-use on many challenging problems. By understanding this material and the connections to last week and to the week ahead, students will be well positioned to have mastered this first course in the specialization
What's included
3 videos1 reading1 quiz1 assignment1 discussion prompt
Change points are locations where the previously stationary distributions of the last two modules shift to a new distribution In a manufacturing line this could be due to a new batch of materials that arrive with different characteristics, so the failure rate changes.
What's included
2 videos1 reading1 quiz1 assignment1 discussion prompt
Markov chains describe a sequence of state changes. They are often used to describe complex transitions between states and are a primary modeling tool for improving understanding of a complex system. We will use them as a model for how sequential data may be produced by a more complex system.
What's included
3 videos1 reading1 quiz1 assignment1 discussion prompt
The next step in modeling ability is Markov processes with decisions. This connects to modern research in reinforcement learning and enables optimization over the sets of decisions for an optimal outcome. In this last week of the first course we will cover the basics of how these Markov Decision Processes can be parameterized and what they mean.
What's included
2 videos1 reading1 quiz2 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
Google Cloud
Johns Hopkins University
LearnQuest
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