Really really useful

What a good channel i've come across. This will definitely become a part of my library to extend my problem solving toolbox as a developer. Thank you!

I struggled to understand this in my class, I'm glad I watched this video. These are very helpful.

What is the link to the next video immediately after this?

He mentioned the next class at the end. What video is the next class?

remember when??

Thanks so much for this video! It was very insightful for me👍🏾

11:49 Can anyone tell me why phi(A, C) = P(C|A) and not P(A)P(C|A)?

Heat Death

Never take this down Bert. I send it to everyone asking me what is a good overview of the main models for classic ML, even if it's 9 years old.

❤❤❤❤❤

Thanks for helping me❤❤❤

awesome

what is 'phi' sir

thank you for this brilliant explanation. I wished there was a Question with solution to practice on.

very unclear and comfusing using venn diagrams to represent some of the probabilities and giving detail example of the math using numbers to show how it runs would be of great help, for people discovering the subject. I am fairly sure this is a great video for people who already understand the subject or have some grapst on it. But for new comer it is very confusing. not to mention the rise in difficulty between the first part which is quite easy to understand (although venn diagrams would help) and the second part which looks like elvish.

Very simple explanation, thans !

The bot sacrificed the bishop because of the depth 3 search. It saw bishop to b5, pawn takes bishop and then queen takes rook. It didn't see you can recapture the rook, that's depth 4. It thoight it can take a rook for a bishop and pawn.

Thanks for great video! Helped me a lot in understanding this stuff for my Uni course :)

you suck at following any sort of linear pace. Fuck youtube videos

great stuff!!

from Bihar (INDIA)

With the given respect to you, but not to the people that created this "Variable elimination" thing, this variable "elimination" sounds like bullshit because you are already computing all the possible states of the variable that you are going to eliminate, meaning that you aren't eliminating nothing already. Or I'm wrong?

absolutely useless.

Thanks for the great explanation! Finally understood the implementation of HMM`s

6:52

🎯 Key Takeaways for quick navigation: 00:00 📊 Probabilistic graphical models, such as Bayesian networks, represent probability distributions through graphs, enabling the visualization of conditional independence structures. 01:34 🎲 Bayesian networks consist of nodes (variables) and directed edges representing conditional dependencies, allowing the representation of full joint probability distributions. 03:21 🔀 Bayesian network structures reveal conditional independence relationships, simplifying the calculation of conditional probabilities and inference. 09:10 🧠 Naive Bayes and logistic regression can be viewed as specific Bayesian networks, with the former relying on conditional independence assumptions. 11:55 📜 Conditional independence is a key concept in Bayesian networks, defining that each variable is independent of its non-descendants given its parents. 15:15 ⚖️ Inference in Bayesian networks often involves calculating marginal probabilities efficiently, which can be achieved through variable elimination, avoiding full enumeration. 23:54 ⚙️ Variable elimination is a technique used in Bayesian networks to replace summations over variables with functions, reducing computational complexity for inference. 24:05 🧮 Variable elimination is a technique used to compute marginal probabilities efficiently by eliminating variables one by one. 28:07 ⏱️ In tree-structured Bayesian networks, variable elimination can achieve linear time complexity for exact inference. 29:02 📊 Learning in a fully observed Bayesian network is straightforward, involving counting probabilities based on training data.

This is so good!!!

What is the name of textbook

thank you so much, you explain the subject very well and have helped me to understand..

My professor for AI explained this so badly that I had no idea what was going on. Thanks for this in-depth and logical explanation of these topics

Fantastic video! Thanks a lot!

Thank you for the good explanation :D

the BEST lecture about fairML

Great for getting up to speed again!

i dont want to learn technology but i want you so bad bro.

This very nice, instead in LDPC decoder, this numerical stuff happen so very often. Back then I use matlab vpa but it very slow. Thank you so so much !!

Fantastic video Thanks so much for making this

Next time prepare for streams before going live. Check "Machine Learning with Phil"

Can you please update the recent code on your github repository. The one currently on github is to old. And get and error `ValueError: shapes (395,) and (790,) not aligned: 395 (dim 0) != 790 (dim 0)`

Can you please let me know what software do you use for writing ? -- Is it notes in Zoom ? Seeing your face surely makes it better.

Is this playlist still useful if I want to use deep learning instead of reinforcement learning ?

Top tier video without a doubt.

When a flock leaves a tree, it is as if the tree went bare!

Very impressive, you make the model crystal clear, and I know that compute bayesian network is nothing than that to calculate a probability (for discrete variables), or a probability distribution (for continuous variables) efficiently.

Greate explanation! Thank you for the video!

very good explantation!

Exactly what I was looking for. Can't wait to get some free time in a few weeks to start the project myself.

Thank You! what is your chess rating?

the hardest part of python language is that, you can't easily pass the variables/function to parent/child/sister levels, and that's your problem too at 34:56