Hey, this course looks really interesting! Although I'm hesitating to take it because I'm not sure what the difference is between Machine Learning and Deep Learning. Can anyone explain?
Machine Learning is a catch-all term for optimizing statistical models with data.
Simplest example that you are very likely already familiar with is that of a 'best fit line' to some xy scatter plot. This starts by making an assumption (model choice) that the relationship between `x` and `y` is linear, e.g. `y=mx + b`, then you can use data (xy points) to figure out the most likely values for `m` and `b`. You can then make predictions for new `x_new` values by plugging them into your known line to get `y_new`.
Machine learning often manifests in a two step process: first feature extraction, and then fitting features to a desired output. Deep learning combines these as an end-to-end process to eliminate 'human in the loop' problems that occur from feature extraction.
Example: you want to predict who should win a chess game in a given board state
Feature extraction (what information you think matters): what pieces does white have, what pieces does black have, is white in check, is black in check, how many valid squares can white king move to, how many valid squares can black king move, etc...
* Fitting: make an assumption about the relationship between features and outcome (model choice), fit model using data (features, outcome)
The Deepblue 2 model that played Kasparov used around 8000 features (not sure if this is the feature vector size or # of features). As you can imagine, feature extraction is highly dependent on expert knowledge of the problem and will often fail to cover unknown situations/cases.
Deep learning models aim is to avoid limitations of expert knowledge by using raw data (e.g. occupancy of each square on a chess board) and extract features implicitly rather than relying on explicit human formulas. It has also opened up new possibilities for areas where expert knowledge has made little progress in the past (e.g. there is not much an expert can say about what pixel features are might indicate a dog/cat is contained in an image).
Though this end-to-end nature also makes it harder to spot biases and errors. There is some work that first trains with deep learning and then tries to infer a simpler model that is understandable for humans.
I took half of ML before. The pace is a bit slower and topics are slightly different. ML goes into using random forests and focuses more on working on structured data. I think if you have programming experience and are more interested in Deep Learning than the Deep Learning course is great.
