<p>I was tasked to help in the practical sessions of the Machine Learning course in my university, the plan was to give exactly the same materials as <a href="https://www.coursera.org/learn/machine-learning" rel="nofollow noreferrer">this course</a>. We should be teaching the assignments of this course as lab sessions.</p>
<p>The main problem is that this course is being given as the main source for teaching ML in the university(lectures and lab session), I don't have any previous teaching experience but I personally think that it is better  to teach using some famous textbook like Ethem Alpaydin's Intro to ML book.</p>
<p>I'm looking mainly for the pros and cons of both approaches if you have any experience with any(or both) of them.</p>

CC BY-SA 4.0
2022-03-01T20:46:41.263Z
Hmmm. Do you have a copyright problem? In much of the world you would.
Buffy
CC BY-SA 4.0
2022-03-02T04:35:05.780Z
Thanks for the note. But unfortunately in my country we don't have any mechanism to apply copyright law.
subspring
CC BY-SA 4.0
2022-03-02T15:26:55.133Z
Sorry, but there are also ethical issues around taking someone else's content without permission.
Buffy
CC BY-SA 4.0
2022-03-02T16:08:02.887Z
You are absolutely right, I will contact them and ask for their permission. But even if I do that, the whole country would still steal academic literature and no one would be able to change a thing. For example I really want to buy tons of textbooks, but we don't have amazon or any online payment service, what should we do then? I'll make sure to ask for permission and to acknowledge the original sources to students.
subspring
CC BY-SA 4.0
2022-03-23T11:13:59.923Z
Umm was that book really published in  2004!? I feel like anything pre-resnet (2015) would be pretty useless for students that intend to contribute to the field or work in industry. Maybe a statistics book would be better before jumping into modern ml.
Navin
CC BY-SA 4.0
2022-03-23T11:31:57.710Z
The third edition of the book was published in 2014, so yeah it is kinda of old. Thanks for the suggestion.
subspring
CC BY-SA 4.0
2022-03-23T12:12:34.867Z
Yeah 2014 is pretty old. I might be biased since my research is focused on something that only became practical in the last decade (GANs) but if I were you, I’d do a quick refresher on ml/statistics (logistic regression, ridge regression, bias/variance tradeoff), and then skip ahead to deep learning. The linked course looks decent to me. You could probably skip/skim over SVMs, but besides that it looks like a reasonable intro to ML.
Navin
CC BY-SA 4.0
2022-03-23T13:58:18.810Z
Thanks a lot, your suggestions are really helpful.
subspring
<teaching-analogy><machine-learning>
ML Andrew Ng course as practical university course
# ML Andrew Ng course as practical university course

(Asked by: username_0 on 2022-02-27)

I was tasked to help in the practical sessions of the Machine Learning course in my university, the plan was to give exactly the same materials as [this course](https://www.coursera.org/learn/machine-learning). We should be teaching the assignments of this course as lab sessions.

The main problem is that this course is being given as the main source for teaching ML in the university(lectures and lab session), I don't have any previous teaching experience but I personally think that it is better to teach using some famous textbook like Ethem Alpaydin's Intro to ML book.

I'm looking mainly for the pros and cons of both approaches if you have any experience with any(or both) of them.

**Tags:** teaching-analogy, machine-learning

> **Comments:**
> **username_1**: Hmmm. Do you have a copyright problem? In much of the world you would.  
> **username_0**: Thanks for the note. But unfortunately in my country we don't have any mechanism to apply copyright law.  
> **username_1**: Sorry, but there are also ethical issues around taking someone else's content without permission.  
> **username_0**: You are absolutely right, I will contact them and ask for their permission. But even if I do that, the whole country would still steal academic literature and no one would be able to change a thing. For example I really want to buy tons of textbooks, but we don't have amazon or any online payment service, what should we do then? I'll make sure to ask for permission and to acknowledge the original sources to students.  
> **username_2**: Umm was that book really published in  2004!? I feel like anything pre-resnet (2015) would be pretty useless for students that intend to contribute to the field or work in industry. Maybe a statistics book would be better before jumping into modern ml.  
> **username_0**: The third edition of the book was published in 2014, so yeah it is kinda of old. Thanks for the suggestion.  
> **username_2**: Yeah 2014 is pretty old. I might be biased since my research is focused on something that only became practical in the last decade (GANs) but if I were you, I’d do a quick refresher on ml/statistics (logistic regression, ridge regression, bias/variance tradeoff), and then skip ahead to deep learning. The linked course looks decent to me. You could probably skip/skim over SVMs, but besides that it looks like a reasonable intro to ML.  
> **username_0**: Thanks a lot, your suggestions are really helpful.  

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## 0 answers

