Introduction to Machine Learning - 01 - Baby steps towards linear regression

Lecture 1 in the Introduction to Machine Learning (aka Machine Learning I) course by Dmitry Kobak, Winter Term 2020/21 at the University of Tübingen.

[@richardm5916]

You are the best teacher in the world thanks

[@hypercortical7772]

This whole channel is amazing. Thank you so much

[@j.adrianriosa.4163]

This lecture was so well explained!
The baby-steps approach is so clever.
By understanding the simplest cases one can grow from there!
Thank you Dmitry!

[@rembautimes8808]

This is a very good lecture. The discussion of the Loss Function is the first time I’ve see someone explain it so clearly and gives the intuition of what argmin really means

[@SAHarry]

Thanks so much! The simple introduction makes all the generalized equations a lot easier to understand!

[@user-ew8tl4qm1n]

Absolutely fantastic explanation. Recommendation of freely available literature is golden, too!

[@samuelthimoteosilvasantos473]

Wonderful explanations! Make a hard subject appears simple.

[@svetlananalobina8652]

Отличное объяснение! Спасибо ❤

[@zuhail1519]

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!

[@Thierry_Taguela]

Thanks for this channel

[@mioszdaek1583]

Great lecture :)

[@nirash8018]

Thank you so much! Way better than my Professor at Uni Ulm who just spams you with formulas

[@justiceessiel6123]

you honestly do not need a prerequisite to understanding what he is saying. You just need to listen and follow. google the terms you do not understand and just take notes the understanding actually come after a certain period of time

[@amalalsaeh4894]

thank you

[@khuongtranhoang9197]

Do you have the link to the course?

[@Info.QamarChishti]

Good 👍

[@electric7309]

🎯 Key Takeaways for quick navigation:
00:11 📚 Introduction to the Course
- This section introduces the "Introduction to Machine Learning" course.
- The course aims to provide a basic understanding of machine learning concepts.
- It's designed to prepare students for more advanced machine learning courses.
03:32 🧠 What is Machine Learning?
- Explains the definition of machine learning as the study of algorithms that improve through experience.
- Contrasts traditional problem-solving approaches with machine learning.
- Discusses the difference in emphasis between statistics and machine learning.
11:56 🕵️ Types of Machine Learning Problems
- Introduces the three main types of machine learning problems: supervised, unsupervised, and reinforcement learning.
- Focuses on supervised learning and briefly mentions unsupervised learning.
- Explains that reinforcement learning is not covered in this course.
14:59 🔍 Linear Regression as a Starting Point
- Discusses why the course begins with linear regression, a simple and classical method.
- Introduces the concept of a loss function for linear regression.
- Mentions the idea of "baby linear regression" where the intercept is constrained to zero.
21:24 📈 Optimization and Finding the Minimum
- Discusses the concept of finding the minimum of the loss function to estimate the beta values in linear regression.
- Explains that the loss function results in a quadratic polynomial.
- Highlights the need to find the estimate (beta hat) given the training data.
23:39 🧐 Gradient Descent in Linear Regression
- Gradient Descent is a method to find the minimum of a function.
- The update rule for Gradient Descent involves a learning rate.
- The choice of learning rate impacts the convergence of Gradient Descent.
32:50 🧐 Extending to Simple Linear Regression
- Simple Linear Regression involves two parameters: the slope (beta1) and the intercept (beta0).
- The loss function for Simple Linear Regression forms a 3D surface.
- Gradient Descent can still be used with partial derivatives to optimize in multiple dimensions.
Made with HARPA AI

[@adilsolimn3437]

Hello , are the slides for the videos lectures available . I know the slides for other courses in the series are available but not this one ?

[@mememe8385]

machine learning vs pattern recognition?

[@ishangoinyambo6523]

great lecture!! 29:00 I think you increase beta to decrease the loss since the derivative is negative

[@mahmoudkhairy579]

It's true but this equation is true for increases beta or decrease it's depends on the signal slope of the curve of MSE

[@DockSchrem]

beta_1 = ((1-n).Sum(y_i.x_i))/(n.Sum(x_i^2) - Sum(x_i)^2), beta_0 =(-Sum(y_i) - Sum(x_i).beta_1)/n. Did someone solve the exercise in the end.

[@babadook4173]

is there any website of this course? can we access the notebooks?

[@dilsydiltak0101]

i want to ask something about the course...there are many courses related to ML on this channel but where to start....what is the 1st course that i pick???anybody please tell me

[@tilakkalyan]

1) Basics of ML
2) Basics of Maths (Statistics)
3) Python Basics

[@dilsydiltak0101]

@@tilakkalyan there are some courses on this channel like:
Probalistic ML
Statistical ML
Math for ML
Intro to ML
and many more related to ML but i want to ask that from these all course what should be the 1st one to learn...

[@dfk1991]

Нашел интересный жорнал про всякие статистические данные, довольно занятно)

[@godfreypigott]

Terrible microphone.

[@antonvesty2256]

The mistake was putting the mic to his nose instead of mouth...

[@JamesSmith-bo3po]

Focus on the content.

[@godfreypigott]

@@JamesSmith-bo3po Sure - once I am able to hear it.

[@itsme-xo4ku]

​@@antonvesty2256😂