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In these sets of MOOCs, there was also Machine Learning taught by Andrew Ng and I had your same Widom like experience with Andrew Ng. After taking so many cours

In these sets of MOOCs, there was also Machine Learning taught by Andrew Ng and I had your same Widom like experience with Andrew Ng. After taking so many courses in life - physically and remotely - I can safely say that Andrew Ng is the best teacher I've come across. The best part was how we actually gave insights in Machine Learning rather than leaving it like something esoteric. It changed my perspective and added a lot of value to my professional career. Recently, I tried to take the Neural Networks from Jeff Hinton and I realized how much I missed Ng's lectures.

icc97 10y ago
I've had exactly the same struggle. Andrew Ng was truly awesome, better than any lecturer I had. Jeff Hinton's course is just a real struggle to complete. It's more than just the lectures, the thought and relevance that goes into the problem sets is a big difference too. It's great to hear that Jennifer Widom's course is on a similar level. Perhaps I should dust up on my database knowledge.
sweezyjeezy 10y ago
I took Geoff Hinton's Coursera course about 3 years ago, and it remains my favourite online course I've ever taken. While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues. I also loved his wry sense of humour and turn of phrase. I felt that Andrew Ng left out a lot of the details that I was curious about. For example, I remember he introduces logistic regression and cross-entropy, but he kind of just writes down the equations and says they differentiate nicely, he never explains where they actually come from, or even points the interested viewer where to look for more information (short answer : information theory, also this post https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/ https://terrytao.wordpress.com/2016/06/01/how-to-assign-part... is a great explanation for cross entropy)
- denzil_correa 10y ago
  > While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues. This is the same argument people gave for Machine Learning but Andrew Ng showed us otherwise. > I felt that Andrew Ng left out a lot of the details that I was curious about. This is exactly the beauty of Ng's ML course. The fact that Andrew Ng made you curious makes CS229A (Applications of Machine Learning) a very successful course. Now, if you want to satiate that curiosity I will recommend you CS229 [0]. It contains all the theory and math and YET that course is as beautiful as CS229A. This further proves the point that you do not need theory to be boring to achieve your objectives. If you want to teach something, you will find a way. Au contraire, Jeff Hinton's course makes me not want to even touch or understand Neural Networks again. FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites? [0] https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599
  - sweezyjeezy 10y ago
    > FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites? The probability, linear algebra and calculus material required to understand this should be covered in undergraduate courses. Which part exactly did you find so hard?
    - denzil_correa 10y ago
      This isn't about the content but the structure and explaining the intuition behind algorithms. There are insights behind every algorithm which needs to be put down explicitly in simple English; not mathematical symbols. A course should be accessible to everyone given the prerequisites. Just because a course is easy for me, does not mean its a good course. The Hinton course is not palatable and you can clearly see that in the discussion forums. Andrew Ng's CS229 course goes beyond the mathematics and explains the magic behind these algorithms. Unfortunately, I know many people who use the mathematics trope to not share these insights and keep them closely guarded to their chest.

icc97 10y ago

I've had exactly the same struggle. Andrew Ng was truly awesome, better than any lecturer I had. Jeff Hinton's course is just a real struggle to complete. It's more than just the lectures, the thought and relevance that goes into the problem sets is a big difference too. It's great to hear that Jennifer Widom's course is on a similar level. Perhaps I should dust up on my database knowledge.

sweezyjeezy 10y ago

I took Geoff Hinton's Coursera course about 3 years ago, and it remains my favourite online course I've ever taken. While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues. I also loved his wry sense of humour and turn of phrase. I felt that Andrew Ng left out a lot of the details that I was curious about. For example, I remember he introduces logistic regression and cross-entropy, but he kind of just writes down the equations and says they differentiate nicely, he never explains where they actually come from, or even points the interested viewer where to look for more information (short answer : information theory, also this post https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/ https://terrytao.wordpress.com/2016/06/01/how-to-assign-part... is a great explanation for cross entropy)

denzil_correa 10y ago
> While I do understand that it may have felt a bit theoretical and heavy, I also think that this theory is more important to understand in neural networks than in other areas of ML e.g. for debugging issues. This is the same argument people gave for Machine Learning but Andrew Ng showed us otherwise. > I felt that Andrew Ng left out a lot of the details that I was curious about. This is exactly the beauty of Ng's ML course. The fact that Andrew Ng made you curious makes CS229A (Applications of Machine Learning) a very successful course. Now, if you want to satiate that curiosity I will recommend you CS229 [0]. It contains all the theory and math and YET that course is as beautiful as CS229A. This further proves the point that you do not need theory to be boring to achieve your objectives. If you want to teach something, you will find a way. Au contraire, Jeff Hinton's course makes me not want to even touch or understand Neural Networks again. FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites? [0] https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599
- sweezyjeezy 10y ago
  > FYI, I have a Ph.D in Computer Science and I work as a Data Scientist. If this is my view, imagine what it would be like beyond the us elites? The probability, linear algebra and calculus material required to understand this should be covered in undergraduate courses. Which part exactly did you find so hard?
  - denzil_correa 10y ago
    This isn't about the content but the structure and explaining the intuition behind algorithms. There are insights behind every algorithm which needs to be put down explicitly in simple English; not mathematical symbols. A course should be accessible to everyone given the prerequisites. Just because a course is easy for me, does not mean its a good course. The Hinton course is not palatable and you can clearly see that in the discussion forums. Andrew Ng's CS229 course goes beyond the mathematics and explains the magic behind these algorithms. Unfortunately, I know many people who use the mathematics trope to not share these insights and keep them closely guarded to their chest.