Machine Learning course- Shai Ben-David: Lecture 1

[@BBPFamily-h2o]

what is learning: 8:07 memorization 14:15 generalization 15:41 prior knowledge: 29:34 relevant vs irrelevant features 31:14 prior knowledge vs data need 33:53 types of learning: 44:02 ML and AI 54:20 Prerequisite for this ML program 56:00 prediction rule 1:12:14 data generation assumption 1:13:38 realizability assumption: 1:14:50 measure of success of prediction rule 1:16:33

[@kiranightray4970]

my left ear enjoyed machine learning.

[@samdavepollard]

That's your training data sorted. Right ear can be your test. :-)

[@SadamHussain-vl8pc]

download video and play on vlc, there is option in "Audio->Sterio mode" & select left , hope it will fine.

[@educated.fahim96]

thank you

[@Putra-zy7vf]

hahaha.. it thought i's just me

[@intSevero]

Just pull your headphone jack midway out and it will become "mono on both sides"

[@JordanBoydGraber]

Content starts at 8:07

[@vivekpraseed918]

thanks

[@nimaabbasi1927]

there's a special place in heaven for people like you

[@gorgolyt]

@@nimaabbasi1927 You could also read the video description

[@pankajpatel8490]

This man is brilliant. There will be math, but he provides intuition. Please NEVER delete the videos for this class.

[@armankabiri9123]

This detailed machine learning course from University of Waterloo, will guide you through basics & advanced chapters of machine learning. It’s a conceptual course which will educate you on mathematical relations in ML algorithms. It has been taught by multiple professors including Shai Ben David, author of book Understanding machine learning. It covers topics such as linear regression, bayesian, trees, clustering, neural networks, ensemble, hidden markov model and much more. Check out the other course material here. The first video in an introductory to the course so, feel free to skip the first 8 mins of the video.

[@mohammedalmatary380]

should we have any prerequisites for the course thank you!

[@CiscoTokyo]

We are following His book at our university (BRAC University)

[@jingyugao]

Does anybody have Solution Manual Understanding Machine Learning: From Theory to Algorithms (Shai Shalev-Shwartz & Shai Ben-David). Thank you!

[@harshshrivastava6160]

1:11:43 - Prof. meant unit square, I guess.

[@shizidushu8987]

Please add subtitles to the video series. Thanks.

[@zenicv]

for setting the expectations: 5:52

[@STIVESification]

Awesome, thanks for posting lecture

[@eugeniures7593]

Ce cauți aici, țigane?

[@required.]

Such a great lecture , from great professor..

[@dixantmittal]

Thank you prof!

[@mikeskater94]

Thank you soooo much for sharing this with us!!

[@akolec]

Related course: webcourse.cs.technion.ac.il/236756/Spring2014/en/ho_Lectures.html

[@kparag01]

Sir what are prerequisites to take this course???

[@yogiete9656]

probability is the most important prerequisite

[@yashsvidixit7169]

SC/ST certificate

[@namangupta2228]

@@yashsvidixit7169 kya bachodi kar raha hai.

[@yashsvidixit7169]

@@namangupta2228 kyun bhai

[@namangupta2228]

@@yashsvidixit7169 because he is lecturer from Canada not teaching in any Indian university.so no one's caste certificate can help in securing admission over there.

[@sagaradoshi]

Why did Prof write domain set as [0,1]^2 at around 1 hr 10 min

[@harshshrivastava6160]

"[0,1]^2" represents the set of all points (x, y) where both x and y are real numbers in the closed interval [0,1]. Graphically, this corresponds to a unit square in a two-dimensional coordinate system, where both the x and y coordinates range from 0 to 1.

[@goxherecorp7316]

what is the corresponding text book for this course so we can follow along?

[@understandingmachinelearni6331]

The book is "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David. It's available online for free at www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

[@orosalsero]

what happened no audio....

[@RahulMadhavan]

Someone tell the cameraman that the camera should point at the board and not at the professor

[@xiangkunhu1058]

Enjoy 1.25 speed.

[@qizhang9578]

How can we find the assignments?

[@tomlynd8836]

Are lecture notes publicly available?

[@deshraj3001]

Where can I get the assignments for this course? There is no webpage available.

[@ranjan_akash]

www.amazon.in/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132

[@smartboyvijey]

@@ranjan_akash That course is taught by a different lecturer - Pascal Poupart

[@mueez.mp4]

1:07:40

[@papachoudhary5482]

Thanks!

[@TheAvithal]

1:08:44 papaya testing

[@AyushSingh-vj6he]

25:05

[@陳民勝-z5z]

no voice

[@choatezhao9905]

Is this a undergraduate course or graduate course?

[@TDRinfinity]

Fourth year undergraduate

[@choatezhao9905]

Fine, thx

[@shreerangaraju1013]

cs 485 - undergrad cs 685 - grad

[@MrMintu1971]

no sound?

[@mannuscript]

Only from left earphone

[@rohitgavirni3400]

How religion works @ 22:18

[@lorink5937]

yay heb accent ^^ :-)

[@muratcan__22]

thanks!