Types of Machine Learning 1
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- เผยแพร่เมื่อ 8 ต.ค. 2024
- This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels.
Book website: databookuw.com/
Steve Brunton's website: eigensteve.com
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
Excellent material for beginners and advanced ML practitioners. Clearly explained !
Glad you think so!
Despite the extensive knowledge and effective teaching, the most impressive part of this video is definitely how fast he was able to draw cats and dogs in small boxes. Wish I could become just like him.
nice explanation and the way you write backwards is really fantastic. More power to you!
Thank you for the video!
The way you write backward and the handwriting is better than mine
Awesome content! You do a great job tackling these "next level" topics beyond just a surface description.
Great explanation, thank you very much sir
HI sir I liked your tutorials on fourier and wavelets yet would you kindly add a tutorial in the near future if possible on the quality of different ML methods / algos including RL and which one suits best time series data with the following empirical properties ; 1- non-stationarity 2- order matters 3- low signal to noise ratio so SD for example is a some multiple of the mean / central tendency measure , your input is highly appreciated
As always great.
all the examples that you give to explain data science and ML concepts are from aerospace or mechanics domain except for cat dog example and those aerospace/mechanics domain examples becomes difficult to understand for a person not having that background so it would have been better if you gave simpler examples that don't require that or complex domain knowledge.
In the example of "supervised learning with labelled data" given in this video, a labelled image of a cat goes in, the NN adjusts its weights and biases so a classification of "cat" comes out. But let's say I want to train a neural network so it can get good at playing tic tac toe against some opponent. When training this NN, the inputs are the values of each of the 9 squares of the game (each square is a cross, a nought, or empty). The output is the move to be made by the NN. Questions: Is this input data considered "labelled"? Is this considered "supervised learning"?
dude i have been thinking about this comment for so long, and honestly i have no idea. i suppose that considering how simple the game is, given that you would be ready to spend a lot of time labelling the correct response to what is happening on the board, it could be possible. But then, i dont know how well would that work. For sure, reinforced learning would be a more obvious choice here, but supervised? hmm, maybe...
@@adamdudkiewicz6444 using labelled data to "teach" a NN is called "supervised learning". This is not the only machine learning method. Another method, "reinforcement learning" could be used to teach a NN to play tic tac toe. This learning method doesn't use labelled data. Instead, the NN learns by receiving "reward signals" based on how accurate its output data is.
@@adamdudkiewicz6444 maybe you mean semi-supervised
when you realise he's writing backwards...
He is writing on glass
@@aayushparashar4143 naaah! he easily build a model in his brain to write in backwards
its a mirror, hes writing normal
@@farmerfromwisconsin7256 if it's a mirror, why can't we see the camera?
It's a glass for normal writing. Then we shall see the flip words. The reason we see the non-flip words is because the video was post-produced with flipping.