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.
Durée limitée ! Obtenez Coursera Plus avec 30 % de réduction.
Réinitialiser. Réinventer. Atteindre de nouveaux objectifs professionnels. Profitez de votre offre maintenant.
Data Science Decisions in Time: Using Data Effectively
Ce cours fait partie de Spécialisation Data Science Decisions in Time
Enseigné en Anglais
Instructeur : Thomas Woolf
Inclus avec 
Cours
Expérience recommandée
Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Control Chart
- Catégorie : Testing for Vaccines
- Catégorie : Wald's ideas for stopping
- Catégorie : A:B testing
- Catégorie : working with sequential data
Détails à connaître
Ajouter à votre profil LinkedIn
août 2024
5 quizzes, 6 devoirs
Cours
Expérience recommandée
Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées
Élaborez votre expertise du sujet
- Apprenez de nouveaux concepts auprès d'experts du secteur
- Acquérez une compréhension de base d'un sujet ou d'un outil
- Développez des compétences professionnelles avec des projets pratiques
- Obtenez un certificat professionnel partageable
Obtenez un certificat professionnel
Ajoutez cette qualification à votre profil LinkedIn ou à votre CV
Partagez-le sur les réseaux sociaux et dans votre évaluation de performance
Il y a 5 modules dans ce cours
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.
Inclus
5 vidéos2 lectures1 quiz1 devoir1 sujet de discussion
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
Inclus
3 vidéos1 lecture1 quiz1 devoir1 sujet de discussion
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.
Inclus
2 vidéos1 lecture1 quiz1 devoir1 sujet de discussion
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.
Inclus
3 vidéos1 lecture1 quiz1 devoir1 sujet de discussion
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.
Inclus
2 vidéos1 lecture1 quiz2 devoirs1 sujet de discussion
Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Data Analysis
Google Cloud
Johns Hopkins University
Johns Hopkins University
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Ouvrez de nouvelles portes avec Coursera Plus
Accès illimité à plus de 7 000 cours de renommée internationale, à des projets pratiques et à des programmes de certificats reconnus sur le marché du travail, tous inclus dans votre abonnement
Faites progresser votre carrière avec un diplôme en ligne
Obtenez un diplôme auprès d’universités de renommée mondiale - 100 % en ligne
Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires
Améliorez les compétences de vos employés pour exceller dans l’économie numérique
Foire Aux Questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.