nice topic to watch, thanks for sharing

this is the best LDA video I have seen. thank you so much.

Thanks.

10:47

On the slide of 33:00 we are using the Jacobian instead of the hessian. When the network only has a single output and we use the least squares loss function would the newton step collapse to gradient descent with the gradient divided by its length? It feels like we are just throwing away all curvature information at this point

Nice content just wondering why your views are so low

Excellent presentation

Very nice lecture ! Thank you very much !

do anyone have the exercise of this course?

man's mind ❌ Human mind ✅

1:20:00

50:55 Bayesian Inference is not hard (c)

The best course as ever... Thanks so much.

The best lecture as ever

In the GMM ELBO , why is the q(z) in denominator missing . The ELBO looks incorrect ?

Cool explanation and visualizations!

I don't understand how A is independent of C|B translated to if i told you B was can you make any statement of A and C independent of each other ?

Absolute goldmine

Title: "Foundations of Machine Learning: Walking Through Linear Regression" Introduction to basic concepts of machine learning - Course aims to prepare students for advanced machine learning courses - Focus on developing key concepts and intuitions behind machine learning Machine learning aims to detect patterns in data and make useful predictions in challenging situations. - Machine learning involves training an algorithm by giving it data and answers, allowing it to discriminate without explicit rules. - The focus of machine learning is on making useful predictions rather than learning about the world. Introduction to different types of machine learning problems - Supervised learning involves labeled data to distinguish classes - Unsupervised learning clusters data without labels, focusing on different kinds of animals Simple linear regression involves predicting a continuous variable based on one predictor. - - It uses a linear function with two parameters - intercept (beta zero) and slope (beta one) to fit the data. - - The loss function for linear regression is the mean squared error, which measures the squared deviation between actual and predicted values and is used to optimize the model. Introduction to Baby Linear Regression with a Single Parameter Beta - The model simplifies linear regression by ignoring the intercept and using only one parameter, beta. - The optimization process involves finding the minimum of a quadratic loss function using baby gradient descent with a learning rate. Understanding the challenges with non-convex functions and choosing the right learning rate in gradient descent. - Non-convex functions can lead to challenges in finding the global minimum using gradient descent. - Choosing the right learning rate is crucial as a large learning rate can cause divergence, while a small learning rate can lead to slow convergence. Explaining gradient descent for simple linear regression - Computing gradient using derivative with respect to beta not x - Utilizing derivative to update beta and converge to minimum point Understanding beta as a vector in two dimensions and its update rule using gradient - Beta can be considered as a vector with two coordinates, beta 0 and beta 1 - The gradient is a vector consisting of partial derivatives along each coordinate

Great lecture thank you! At 39:24, to compute the covariance, I found his explanation incomplete because he doesn't address variation in x. I give a more detailed derivation below. cov(f(x,θ),f(x',θ)) \approx cov(f(x,θ_*) + J(x,θ_*)(θ - θ_*)), f(x',θ_*) + J(x',θ_*)(θ - θ_*)) by Taylor of J in θ = J(x,θ_*) cov((θ - θ_*)), (θ - θ_*)) J(x',θ_*)' by multilinearity of cov = J(x,θ_*) Var(θ) J(x',θ_*)' \approx J(x,θ_*) ψ^-1 J(x',θ_*)' by the Laplace approximation to posterior on θ. As for the negative, is that a typo? I thought he said it was but then he said it wasn't? I'm confused.

40:52

Thank you so much for this great course.

It would be nice to have the option of slides in English and captions in English if possible :)

1:21:28

Would you please make videos in English? I'm from BHARAT 🇮🇳🙏🏻.

Is it correct that the way we define x on slide 10 the order matters? Otherwise p(x|f) would not be a probability distribution. I think this is quite inconsistent with the way x was used before: Not as some vector in {0,1}^n but as the number of glasses-wearing people -> Would be more consistent to use a binomial coefficient as normalization constant.

Danke fürs hochladen!

Tübingen ML has some of the best educational KZbin thumbnails. Usually very clean.

Thanks.

good video

Best video so far on ANOVA

1:23:40

next lecture please

The best video on this topic I have found so far by a large margin. Excellent work!

Hi , Please provide code and excercise of this very nice code.

Can we get course website

You are the best teacher in the world thanks

It was mentioned earlier that the product of two GP is another GP only if it is over the same set of variables (x), and that it is some else if it is over two different set of variables (say x and y). Does not this apply to the prediction step at 1:17:11 (from 2nd to 3rd line)?

Great video!

Having watched quite a lot regression videos I can say confidently this is something which sums up and condenses each and every thing for a beginner to grasp linear regression smoothly(see what I did there?). Thank you so much for making this public!

27:20 Really impressive, especially the pronunciation of Kolmogorov's name.

have you tried with silver nano wire networks ?

awesome

sehr gut

Which book do you guys follow?

can we have the homeward or exercises of this course?

Maike hat das toll gemacht!

in 36:40 sum( f_i(\theta) P_i), P has dimension 3N and f(\theta) has 207? how this multiplication is possible?

Awesome class. But the notation in slide 11 is a bit confusing, what is the advantage of having a unity vector and rotation angle on \omega_j? And, is there a typo on slide 11 should \omega² be (\omega_j)²?

where can i get the code ?