I have mixed feelings about this course. On the theorical side, its does a good job on explaining Markov Decision Problems and its variants, and a very bad job on explaining Reenforcement Learning, which is potentialy the most interessant subject and one of the expectations of most who take the class. On the pratical side, the grading is based on the best 3 out of 4 lab+homework. Homework is hand written problem that you have to solve, and lab is essencially completing code on the lab class. Low efford and easy to get good grade but very dissapointing in terms of engagement, I feel likethis...

Overall Impression

I was expecting more from this course. If I had to choose again, I would probably look for a different course. I took it mainly because it's part of the AI specialization.

The course focuses on AI decision-making and how artificial intelligence determines optimal actions, similar to how a smart vacuum cleaner navigates a space.

Labs and Assignments

• Labs are done in groups of 2 and consist of:

  • A homework assignment
  • A Python programming task

• Both components are relatively simple and focus on the same core concepts. -...

The course had a lot of potential but disappointed me.

It talks about a peculiar part of AI (reinforcement learning) but since we have 4 Labs, and the 4th (about reinforcement learning) is optional, it makes so that you do not invest time in the most 'fun' part.

The lab and homework part is really easy to get a good grade.

For the exam, if you invest time in knowing the theoretical part and the practical part of the course, you can do just fine.

Teaching staff

PADI was taught by Professor Francisco Melo, who is hands down one of the best professors I've had at Técnico. His teaching style is super engaging, and he always makes a point of explaining concepts in various ways with easy-to-understand examples, and reviewing material at the beginning of classes.

Practical classes

The practical classes involved implementing code in notebooks in groups of 2, and helped put concepts into practice, although sometimes it felt like throwing mud (or Copilot code) at the wall...

Syllabus

The PADI course is divided into two main topics: Planning and Learning, always having a focus on 'uncertainty' (meaning probabilities).

The main goal of this course is to teach Reinforcement Learning, in which an agent learns through trial and error (e.g. self-driving cars, robots, etc.). All the course content is presented with this goal in mind.

In the first part, we are introduced to Markov chains. They are a way to model probabilistic systems, i.e. systems where there are various states, and the same action in the same state may lead to different outcomes, with a given...