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# Lecture 1 | Machine Learning (Stanford)

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Education Transforming Lives

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thanks stanford

I wish I had taken this course 10 years ago

I found that coursera guy

HIs signature blue shirt is missing. It's a nascent Andrew Ng!

10 years ago ml class? 😫

Thank you Stanford!, Thank you professor Andrew Ng

In 2008 he says "I am studying it for 15 years " now in 2019 I wonder should I really start this course -_-

What is the meaning of life though!?!?!?!?!?!?!?!??!?!?!

How can one get access to the assignments of the course? I am not able to access the assignments from course website as clicking on the assignment link is leading me to Piazza login page

How different is this version of the course from today's version? Is it very outdated?

Syllabus

http://cs229.stanford.edu/syllabus.html

thank u, sir… 🙂

Love this teacher! It’s incredibly helpful to first explain why you’re learning a particular thing and what it’s useful for. Excellent!

This teacher is good

I wish I were Asian

Does this guy mumble?

I love how they are doing this in the tech world. Stanford seems to be leading this.

Machine Learning: An overview with the help of R software

https://www.amazon.com/Machine-Learning-overview-help-software/dp/1790122627

I remember that desktop background pic from windows…it seems like another life

Imaging people dropping this vastly amazing pool of knowledge just because the lecturer says 'um' from times to times. Imaging these people being the talent recruiter, ggwp.

李彦宏把他招到百度，又离开！为什么

1 an overview of the course in this introductory meeting.

2 linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

3 locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning.

4 Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

5 generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

6 naive Bayes, neural networks, and support vector machine.

7 optimal margin classifiers, KKT conditions, and SUM duals.

8 support vector machines, including soft margin optimization and kernels.

9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.

10 learning theory by discussing VC dimension and model selection.

11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.

12 unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.

13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.

14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).

15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.

16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.

17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations.

18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs.

19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning.

20 POMDPs, policy search, and Pegasus in the context of reinforcement learning.

I am very grateful for these lectures.

Stanford University has done a great service to students and learners around the world by making this videos public. It is a commendable undertaking that helps the progress of humanity as a whole by spreading knowledge throughout the world.

This guy has a Chinese and British accent obviously he’s teaching at Stanford lmao!

Thank you very much Andrew, thank you very much Stanford for uploading these wonderful lectures

3:53 Physics!

My age is 47 and I m not late . All young guys hang on , I m coming

10년전에 이미 머신러닝이 스탠포트에서 진행 중이었다니 놀랍다

That intro is the most 2000s thing ever.

Does anyone have access to the problem sets? I'm able to access everything except for the problem sets.

Whoever sat there listening must be the envy of entire world

What year was Andrew teaching this?

stanford is simply love

2018 – im a dinosaur

What are the prerequisites??

Could someone please reply.

Watching this after 10 years after this video has been uploaded. The concept is still relevant and easily understandbale

Actual definition of machine learning falls on 32nd min

Very nice lecture. Contributed me a lot to document this in short. ML in 5 minutes read (https://www.dataneb.com/single-post/2018/07/09/Artificial-Intelligence-Machine-Learning-Deep-Learning-Predictive-Analytics-Data-Science)

Hi I'm watching at home in 2018!

thanks for sharing, that's really cool

Is whole course published online or just part of course is online?

is this course still good? or should i see other courses and where can i learn how to create practicaly the algorithms with c++ or python. Thank you.

This lectures brings passion on ML

♥

His um made me stop watching after 3min

This guy was 32 years old during this video.

Whee, a Matlab based course! Back when Python wasn't as popular as it is today.

When was this filmed?

To take this series to learn machine learning or coursera one of andrew-ng?