Machine learning - linear prediction

[@manzooranoori2536]

I just love your lectures. Thank you. I'm learning a lot from you.

[@forughghadamyari8281]

hi. Thanks for wonderful videos. please introduce a book to study for this course.

[@kumorikuma]

Great lectures. Thanks so much! I learn much better when I can watch a pre-recorded lecture and pause it / go back at my own pace. (Also can skip 9:30AM class heh).

[@bach1792]

great courses man. thanks

[@panayiotispanayiotou1469]

57:17 The dimensions of the X matrix are n * (d + 1) while for the theta matrix are d * 2. The first x inside the X matrix should be renamed to x_12 instead so that the columns are kept to size d

[@dhoomketu731]

You are right. The parameter matrix to the right of the data matrix X should have d+1 rows.

[@el-ostada5849]

Thank you for everything you have given to us.

[@joehsiao6224]

@45:59 The right hand side of prove looks wrong, specifically xi^TΘ, xi is 1xn, Θ is nx1, there is no need to transpose xi. Unless xi is nx1, but there is no definition of xi before.

[@joehsiao6224]

I found the term is being corrected in another video kzbin.info/www/bejne/h3iylZRvot-Sr6M&ab_channel=NandodeFreitas&t=22m46s

[@charlescoult]

This was an excellent lecture. Thank you.

[@ShaunakDe]

Thanks for the informative lecture.

[@parteekkansal]

Matrix differentiation results are valid when A is symmetric.

[@chihyanglee4473]

Really appreciate this!!

[@letranvinhtri]

Does any one know why in 53:17 difference(0^T * X^t * X0) = 2 * X^t * X * 0? I think it should be (t+1) * X^t * X * 0^t

[@naveedtahir2463]

Combine the theta vectors and you'll understand

[@LunnarisLP]

make sure you understand, what 0^T*X*X^T is. It's really just that. T means a transposed vektor, not power T

[@LunnarisLP]

and check out what X^T*X means and what a dotproduct with itself creates ;-)

[@theneuralshift]

Hi Nando, Thanks for the lecture. How is the undergraduate and the regular machine learning lectures different?

[@adityajoshi5]

Undergrad course contains fundamental topics, straight from basic probability, to bayes theorem. If you don't have background in advanced statistics etc, undergrad one will be better for you..

[@theneuralshift]

Thanks aditya

[@adityajoshi5]

I think I was a little - almost three quarters of a year - late in replying ;)

[@lradhakrishnarao902]

Undergrad courses include concept like linear algebra etc. You can skip those, if you know how to find differentiation and write matrix.

[@amitav1978]

this looks ok but if you could help how to derive the R2 pearson coeficient and then detail that holds good I hope

[@riccardopet]

Hello, I am following your lecture to learn a bit about ML. I was trying to derivate the expression (latex code) \sum_{i=1}^n (u_i - \vect{x}^T_i \theta)^2 that you present on the slide "Optimization Approach". I am not able to derive that expression because I get a very similar expression where the \vec{x} is not transpose. Actually, that should be in agreement with the fact that \theta is defined as a column vector and x_i should be row vector (as also shown in one of the last slides). Thank you very much, your lectures are very well done!

[@ilijahadzic7468]

I noticed the same. The transposition in the last expression on that slide is probably an error. The x_{i} is a row-vector of dimension d, so when multiplied by a column-vector \theta yields a scalar. No need to transpose. The transposition in the first (leftmost) expression is correct.

[@lradhakrishnarao902]

you must be getting the expression y_i^Tx_i * theta. All you need to do is write it as (x_i*theta)T * y_i and your problem is solved. I know, it requires bit of rearrangement, and that's why that particular exercise has been given.

[@KrishnaDN]

fantástico

[@nikolamarkovic9906]

49:40 str 46

[@pradeeshbm5558]

I didn't understand how to draw slop. how to find theta

[@LunnarisLP]

pick any, then you get your loss function, then you minimize the loss :)

[@ashishjain2901]

anyone having ml related to insurance industry. please share

[@yunfeichen9255]

Calculus 101 anyone???? lolz???? a lot of calculus functions...