CS计算机代考程序代写 data mining flex decision tree deep learning algorithm Java Instructions/notes

Instructions/notes
the exam is closed books/notes/devices/neighbors, and open mind 🙂 there are 8 questions, and a ¡®non-data-related¡¯ bonus
there are no ¡®trick¡¯ questions, or ones with long calculations or formulae please do NOT cheat; you get a 0 if you are found to have cheated when time is up, stop your work; you get a 0 if you continue
CS585 Final
Fall term, 12/12/18 Duration: 1 hour
Q
Your score
Max possible score
1
5
2
5
3
4
4
5
5
4
6
5
7
4
8
3
Bonus
1
Total
36

Q1 (3+2=5 points).
a. What is the most straightforward way to transfer (ie. use in a different app, or server or device etc) the results of training a neural network on a large set of training data?
A: Use of a weights-only file, or a config file with architecture+weights – eg. the .m5 weights file is what we used in the ML homework, to transfer the training results to the classification part.
¡®Transfer learning¡¯ is not the right answer – the question mentions ¡®a different app…¡¯, not a different learning domain.
b. Name two practical applications where you might do such transferring (train, use elsewhere).
A: Self-driving car [where the weights are transfered to hardware], a smartphone app to identify birds/mushrooms/flowers/clouds….

Q2 (2+3=5 points). Machine Learning, ie. ¡°ML¡±, has enjoyed runaway success within the last decade, eg. in the form of Alexa, self-driving cars, etc. This is on account of the availability of big datasets, large computing power, adequate memory, and good algorithms/APIs. The modern version of ML is DL, ie. ¡°Deep Learning¡±.
a. What makes DL ¡°deep¡±?
A: The number of intermediate/¡¯hidden¡¯ layers.
b. Even with DL, there is a serious, fundamental, show-stopper flaw in the entire approach to AI. What is it? In other words, what is ML¡¯s/DL¡¯s limitation, one that cannot be solved by faster processing, more memory, more training data, etc?
A: The limitation is that there is no genuine UNDERSTANDING of what the ML/DL is able to learn/classify! For example, the HW5 NN could tell apart cats and dogs, but it does not know that cats and dogs are the most common type of pets [doesn¡¯t even know what a pet is, etc], and, has no way of being ¡®told¡¯. Also, ML/DL has no way to tell apart, correlation in data, from causation (where a part of the data (¡®output columns¡¯), RESULT from factors that the input columns describe).

Q3 (4 points). Consider the following graph:
As you know, graph data can be represented via JSON, to make it be universally readable via a simple parser. Following are two representations; what is a third? You need to show your representation clearly, using valid and complete JSON like below.
A: {
¡°graphData¡±: {
¡°neighbors¡±:[{¡°a¡±:[¡°f¡±,¡±c¡±,¡±d¡±,¡±e¡±,¡±b¡±]}, {..}, {..}, {..}, {..}, {..}]
} }
A variation of the above, also acceptable, would be the elimination of the ¡®neighbors¡¯ key, and simply make the value of ¡®graphData¡¯ be an array of objects like the one shown above.
Another more creative variation (which only works for a fully connected graph!) would be to list each loop, ie. make the value of ¡®graphData¡¯ be [[¡°a¡±,¡±b¡±,¡±f¡±],[¡°a¡±,¡±b¡±,¡±e¡±]…]. Note that there are 3-element loops, 4-element and 5- element ones, and a 6-element one.

Q4 (1+4=5 points). MapReduce is a great architecture, for executing mappers in parallel, then aggregating their outputs via a reducer step; cascading these provides enough flexibility to handle a variety of data-processing tasks.
There is another architecture [not YARN], a ¡°MapReduce++¡±, if you will, which extends the MapReduce paradigm.
a. What is it called?
A: Flink.
b. What are a couple of enhancements that it provides (just name them)?
A: additional transformations (beyond map(), reduce()) such as Join, Filter; additional datatypes (based on Java and Scala).
OK if the answer lists Join, Filter etc. as the ¡®couple¡¯ of enhancements.

Q5 (4 points). Geo-spatial data is inherently 2D, being composed of (lat,long) [or (long,lat)] pairs. What is the fundamental difference in how we set up the DB engine to query such spatial data, compared to standard querying (of non-spatial data)? Illustrate with a diagram.
A: the use of two-level processing – at the first level (filter step), MBRs are used to discard entities outside the query region; at the second stage (refine step), candidates from the first stage are queried exactly, to output the final results.

Q6 (5 points). As you know, there is a variety of algorithms used for data mining. If our data needs to be binary-classified (A or B, yes or no, low or high…), what are our choices, in other words, what algorithms will help us do this? Name/discuss briefly, 5 of them.
A [just names are here – see notes for descriptions]:
a. decision tree
b. clustering
c. regression
d. neural network e. SVM
f. sigmoid (logistic regression) …

Q7 (4 points). Augmented Reality (AR) is where we superpose computer graphical (CG) rendering over live (video) imagery, and modify the graphics to sync with changes in viewpoint (camera motion) – this makes it possible to ¡®pin¡¯ the CG renders on to arbitrary real-world surfaces.
How would you use AR, for data visualization and interaction? Be imaginative – this is an open-ended question.
A: a flat surface on a wall, eg. a blank wall, or a blank whiteboard on it, or a poster… can be used to display 2D visualizations; or, a tabletop or coffeetable etc. can be used to display 3D viz, eg. a multi-linear (two inputs) regression plane, 3D stacked bar charts, SVM plane, 3D clusters…

Q8 (3 points). The use of JSON for representing semi-structured data provides us flexibility, compared to relational tables, when it comes to handling missing data (eg. a customer in a bank does not provide an email address while signing up for an account by walking into a bank, because ¡°the Government will track me because of it¡±). What are some options for handling missing data in a JSON representation [eg. the value for an ¡®Email¡¯ key] of the customer¡¯s account? Name/list 3 valid ways [be imaginative] – they all don¡¯t need to be equally practical/efficient.
A:
a. just leave the missing key out! b. ¡°email¡±:¡±¡±
c. ¡°email¡±:¡±null¡±
The first is the best option.

Bonus (1 point). Look at the flattened cube below on the left. Which of the four shown cubes would produce the flattening?
A: ¡®a¡¯ [look at the photo below, that¡¯s one way to solve – create a paper cube by folding the flattened ¡®T¡¯ pattern:)]