I completed Machine Learning @ Coursera taught by Andrew Ng.
I am in the first week of Probabilistic Graphical Model taught by Daphne Koller. And I am feeling the heat! :)
Although the content and objective of the courses are different. But in my experience Andrew is a better teacher.
Andrew uses very simple sentences. He speaks at a consistent rate. After each class you will always feel like exploring the topic by yourself. Andrew gives you enough to build upon.
Daphne uses very very heavy sentences. Sometimes you will find her speaking at a good intelligent rate and the other times she speaks really fast. She rarely breaths between two sentences. Trust me if you haven't already studied the topic/chapter you won't understand the videos a bit. After going through her videos you won't have enough energy to explore the topic further. :P
One of my fave from Daphne is:
"So here the structure is actually a sparsity in the transition model as opposed to something that manifests at the level of the 2TBN structure which is fairly simple."
ha ha ha
I am in the first week of Probabilistic Graphical Model taught by Daphne Koller. And I am feeling the heat! :)
Although the content and objective of the courses are different. But in my experience Andrew is a better teacher.
Andrew uses very simple sentences. He speaks at a consistent rate. After each class you will always feel like exploring the topic by yourself. Andrew gives you enough to build upon.
Daphne uses very very heavy sentences. Sometimes you will find her speaking at a good intelligent rate and the other times she speaks really fast. She rarely breaths between two sentences. Trust me if you haven't already studied the topic/chapter you won't understand the videos a bit. After going through her videos you won't have enough energy to explore the topic further. :P
One of my fave from Daphne is:
"So here the structure is actually a sparsity in the transition model as opposed to something that manifests at the level of the 2TBN structure which is fairly simple."
ha ha ha
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