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Ending In:
5.5 Hours
When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing—playing chess and Go, driving cars, etc. Reinforcement learning has recently become popular for doing all of that and more. Reinforcement learning opens up a whole new world. It's lead to new and amazing insights both in behavioral psychology and neuroscience. It's the closest thing we have so far to a true general artificial intelligence, and this course will be your introduction.

  • Access 71 lectures & 5.5 hours of content 24/7
  • Discuss the multi-armed bandit problem & the explore-exploit dilemma
  • Learn ways to calculate means & moving averages and their relationship to stochastic gradient descent
  • Explore Markov Decision Processes, Dynamic Programming, Monte Carlo, & Temporal Difference Learning
  • Understand approximation methods
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of calculus, probability, object-oriented programming, Python, Numpy, linear regression, and gradient descent is expected
  • All code for this course is available for download here, in the directory rl


  • Internet required


  • Instant digital redemption
5.5 Hours