Types of Machine Learning 1

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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.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

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Excellent material for beginners and advanced ML practitioners. Clearly explained !

hcordioli

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.

wenhong

nice explanation and the way you write backwards is really fantastic. More power to you!

iftikharkhattak

Thank you for the video!
The way you write backward and the handwriting is better than mine

hannah_

Awesome content! You do a great job tackling these "next level" topics beyond just a surface description.

zacharychristy

Great explanation, thank you very much sir

abdennourbouhounali

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

mikiallen

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.

rishankdabra

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"?

dippy

when you realise he's writing backwards...

DbaybledD

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