Decision Making and Reinforcement Learning from Coursera

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Syllabus

Decision Making and Utility Theory

Welcome to Decision Making and Reinforcement Learning! During this week, Professor Tony Dear provides an overview of the course. You will also view guidelines to support your learning journey towards modeling sequential decision problems and implementing reinforcement learning algorithms.

Bandit Problems

Welcome to week 2! This week, we will learn about multi-armed bandit problems, a type of optimization problem in which the algorithm balances exploration and exploitation to maximize rewards. Topics include action values and sample averaging estimation, 𝜀-greedy action selection, and the upper confidence bound. You could post in the discussion forum if you need assistance on the quiz and assignment.

Markov Decision Processes

Welcome to week 3! This week, we will focus on the basics of the Markov decision process, including rewards, utilities, discounting, policies, value functions, and Bellman equations. You will model sequential decision problems, understand the impact of rewards and discount factors on outcomes, define policies and value functions, and write Bellman equations for optimal solutions. You could post in the discussion forum if you need assistance on the quiz and assignment.

Dynamic Programming

Welcome to week 4! This week, we will cover dynamic programming algorithms for solving Markov decision processes (MDPs). Topics include value iteration and policy iteration, nonlinear Bellman equations, complexity and convergence, and a comparison of the two approaches.You could post in the discussion forum if you need assistance on the quiz and assignment.

Partially Observable Markov Decision Processes

Welcome to week 5! This week, we will go through topics on partial observability and POMDPs, belief states, representation as belief MDPs, and online planning in MDPs and POMDPs. You will also apply your knowledge to update the belief state and employ a belief transition function to calculate state values. You could post in the discussion forum if you need assistance on the quiz and assignment.

Monte Carlo Methods

Welcome to week 6! This week, we will introduce Monte Carlo methods, and cover topics related to state value estimation using sample averaging and Monte Carlo prediction, state-action values and epsilon-greedy policies, and importance sampling for off-policy vs on-policy Monte Carlo control. You will learn to estimate state values, state-action values, use importance sampling, and implement off-policy Monte Carlo control for optimal policy learning. You could post in the discussion forum if you need assistance on the quiz and assignment.

Temporal-Difference Learning

Welcome to week 7! This week, we will cover topics related to temporal difference learning for prediction, TD batch methods, SARSA for on-policy control, and Q-learning for off-policy control. You will learn to implement TD prediction, TD batch and offline methods, SARSA and Q-learning, and compare on-policy vs off-policy TD learning. You will then apply your knowledge in solving a Tic-tac-toe programming assignment.You could post in the discussion forum if you need assistance on the quiz and assignment.

Reinforcement Learning - Generalization

Welcome to week 8! This module covers n-step temporal difference prediction, n-step SARSA (on-policy and off-policy), model-based RL with Dyna-Q, and function approximation. You will be prepared to implement n-step TD learning, n-step SARSA, Dyna-Q for model-based learning, and use function approximation for reinforcement learning. You will apply your knowledge in the Frozen Lake programming environment. You could post in the discussion forum if you need assistance on the quiz and assignment.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces students to decision making theory, which is foundational in computer science and data analysis

Emphasizes practical applications, including detailed walkthroughs of reinforcement learning algorithms such as Monte Carlo and temporal difference methods

Offers learners hands-on experience in implementing reinforcement learning algorithms in assignments, providing valuable practical experience

Provides a gradual introduction to the material, starting with simpler concepts like **Markov decision processes** and gradually progressing to more complex ones like **reinforcement learning**

Suitable for motivated students with some foundational knowledge in calculus and linear algebra

Requires learners to actively engage with the material and complete assignments, which may not be suitable for everyone

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Decision Making and Reinforcement Learning with these activities:

Create a course notes summary

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Enhance understanding and retention by organizing and summarizing course materials, including notes, assignments, and quizzes.

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Review lecture notes, assignments, and quizzes.
Create a concise summary document that highlights key concepts, formulas, and examples.

Brush up on linear algebra

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Strengthen foundational knowledge in linear algebra to support understanding of reinforcement learning algorithms.

Browse courses on Linear Algebra

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Review linear algebra concepts from textbooks or online resources.
Solve practice problems to reinforce understanding.

Review probability theory basics

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Reinforce understanding of basic probability theory concepts to strengthen foundational knowledge for this course.

View Probability & Statistics for Engineers &... on Amazon

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Read through chapters 1-3 of the book.
Solve the practice problems at the end of each chapter.

Five other activities

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Participate in a study group

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Foster collaborative learning and reinforce concepts through discussions and problem-solving with peers.

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Join or form a study group with other course participants.
Meet regularly to discuss course material, solve problems, and share insights.

Learn about decision making under uncertainty

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Extend understanding of how to make informed decisions in uncertain environments, a core concept in this course.

Browse courses on Decision Making

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Follow the tutorials on 'Decision Making Under Uncertainty' from Coursera or edX.
Complete the practice exercises provided in the tutorials.

Attend a machine learning workshop

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Gain exposure to practical applications and recent advancements in reinforcement learning through a workshop.

Browse courses on Machine Learning

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Identify and register for a relevant machine learning workshop.
Actively participate in the workshop, taking notes and asking questions.

Practice solving MDPs

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Improve problem-solving skills in the context of Markov decision processes, a key technique covered in this course.

Browse courses on Markov Decision Processes

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Solve the practice problems on MDPs from the textbook or online resources.
Implement MDP algorithms in a programming language of your choice.

Create a reinforcement learning project

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Apply reinforcement learning concepts to a real-world problem, solidifying understanding and enhancing practical skills.

Browse courses on Reinforcement Learning

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Choose a reinforcement learning problem to work on.
Design and implement a reinforcement learning algorithm to solve the problem.
Evaluate the performance of the algorithm and write a report summarizing your findings.

Career center

Learners who complete Decision Making and Reinforcement Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists leverage the power of data through computation to effect business outcomes. Their primary responsibilities involve collecting, cleaning, and analyzing vast amount of data to uncover hidden patterns and provide insights which guide business decisions. Every industry increasingly relies on the expertise of Data Scientists in an era of digital transformation. Decision Making and Reinforcement learning help data scientists make informed decisions about the collection and analysis of data.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

Operations Research Analysts utilize mathematical and analytical methods to solve complex problems in business and industry. Their work involves building mathematical models to simulate and analyze systems, and they use these models to develop solutions that improve efficiency, productivity and profitability. A strong foundation in Decision Making and Reinforcement Learning is highly valued by employers as it helps build a strong foundation in modeling complex systems and decision-making under uncertainty.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts provide valuable insights and recommendations to businesses and investors. Their primary responsibilities include analyzing financial data and making recommendations on investment opportunities, business plans, and financial strategies. Decision Making and Reinforcement Learning will provide you with a strong foundation in financial modeling and decision making under uncertainty, which are essential skills for success in this role.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts play a crucial role in bridging the gap between business and technology by understanding business needs and translating them into technical requirements. They work closely with stakeholders to gather and analyze requirements, and develop solutions that meet the needs of the business. Decision Making and Reinforcement Learning can provide you with a solid foundation in understanding business processes, modeling complex systems, and making data-driven decisions, which are essential skills for success in this role.

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Management Consultant

Management Consultants provide advisory services to businesses to help them improve their performance. They work with clients to identify and solve problems, develop strategies, and implement solutions. Decision Making and Reinforcement Learning can provide you with a strong foundation in problem solving, decision making, and analytical thinking, which are essential skills for success in this role.

See salaries and explore the career path for Management Consultant

Software Engineer

Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from small personal apps to large enterprise systems. Software Engineers with a background in Decision Making and Reinforcement Learning are highly sought after by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze and interpret data. They work in a variety of industries, including finance, healthcare, and technology. Quantitative Analysts with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers design, build, and maintain the infrastructure that stores and processes data. They work with a variety of data sources, including databases, data warehouses, and data lakes. Data Engineers with a background in Decision Making and Reinforcement Learning are highly sought after by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketing teams, to bring products to market. Product Managers with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in understanding customer needs and making data-driven decisions.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They work on a variety of projects, from developing new algorithms to deploying models into production. Machine Learning Engineers with a background in Decision Making and Reinforcement Learning are highly sought after by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Statistician

Statisticians collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and education. Statisticians with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. Actuaries with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Risk Analyst

Risk Analysts identify, assess, and manage risks. They work in a variety of industries, including finance, healthcare, and technology. Risk Analysts with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Operations Manager

Operations Managers plan, organize, and direct the operations of an organization. They work in a variety of industries, including manufacturing, healthcare, and retail. Operations Managers with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

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Project Manager

Project Managers plan, organize, and execute projects. They work in a variety of industries, including construction, software development, and healthcare. Project Managers with a background in Decision Making and Reinforcement Learning are highly valued by employers, as they have a strong foundation in modeling complex systems and making data-driven decisions.

See salaries and explore the career path for Project Manager