Informações sobre o curso

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Resultados de carreira do aprendiz

33%

comecei uma nova carreira após concluir estes cursos

56%

consegui um benefício significativo de carreira com este curso

33%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Coreano

Resultados de carreira do aprendiz

33%

comecei uma nova carreira após concluir estes cursos

56%

consegui um benefício significativo de carreira com este curso

33%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Coreano

oferecido por

Logotipo de National Research University Higher School of Economics

National Research University Higher School of Economics

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up81%(2,108 classificações)Info
Semana
1

Semana 1

5 horas para concluir

Intro: why should I care?

5 horas para concluir
14 vídeos (Total 85 mín.), 5 leituras, 3 testes
14 videos
Why should you care9min
Reinforcement learning vs all3min
Multi-armed bandit4min
Decision process & applications6min
Markov Decision Process5min
Crossentropy method9min
Approximate crossentropy method5min
More on approximate crossentropy method6min
Evolution strategies: core idea6min
Evolution strategies: math problems5min
Evolution strategies: log-derivative trick8min
Evolution strategies: duct tape6min
Blackbox optimization: drawbacks4min
5 leituras
About the University10min
FAQ10min
Primers1h
About honors track1min
Extras10min
Semana
2

Semana 2

3 horas para concluir

At the heart of RL: Dynamic Programming

3 horas para concluir
5 vídeos (Total 54 mín.), 3 leituras, 4 testes
5 videos
State and Action Value Functions13min
Measuring Policy Optimality6min
Policy: evaluation & improvement10min
Policy and value iteration8min
3 leituras
Optional: Reward discounting from a mathematical perspective10min
External links: Reward Design10min
Discrete Stochastic Dynamic Programming10min
3 exercícios práticos
Reward design8min
Optimality in RL30min
Policy Iteration30min
Semana
3

Semana 3

3 horas para concluir

Model-free methods

3 horas para concluir
6 vídeos (Total 47 mín.), 1 leitura, 4 testes
6 videos
Monte-Carlo & Temporal Difference; Q-learning8min
Exploration vs Exploitation8min
Footnote: Monte-Carlo vs Temporal Difference2min
Accounting for exploration. Expected Value SARSA11min
On-policy vs off-policy; Experience replay7min
1 leituras
Extras10min
1 exercício prático
Model-free reinforcement learning30min
Semana
4

Semana 4

3 horas para concluir

Approximate Value Based Methods

3 horas para concluir
9 vídeos (Total 104 mín.), 3 leituras, 5 testes
9 videos
Loss functions in value based RL11min
Difficulties with Approximate Methods15min
DQN – bird's eye view9min
DQN – the internals9min
DQN: statistical issues6min
Double Q-learning6min
More DQN tricks10min
Partial observability17min
3 leituras
TD vs MC10min
Extras10min
DQN follow-ups10min
3 exercícios práticos
MC & TD10min
SARSA and Q-learning10min
DQN30min

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Sobre Programa de cursos integrados Aprendizagem de máquina avançada

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
Aprendizagem de máquina avançada

Perguntas Frequentes – FAQ

  • 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.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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