GPT-2 Generated “Machine Learning” Article 132

Input: Examples of Machine Learning

Generated Text: Algorithms – A Review
Machine learning algorithms design and test a prediction, such as the classification of individual images into cats from green backgrounds, using the data. The prediction is compared with the observed data. If the comparison is accurate, the prediction is deemed accurate and the training data provided for the learning algorithm.
Classification – Predicts the probability that an individual is a male [OC] or a female [XY]
Regression Line – Predicts the probability that an individual is a male [OC] or a female [XY]
Hierarchical clustering – Predicts the population percentage of an original dataset composed of males and females
Neural network – Predicts the population percentage of an original dataset composed of males and females [JWLM]
Deus II Machine Learning Challenge
The  Machine Learning Department at the University of Toronto invites contestants to submit possible answers to the challenge below by 11:59 p.m. ET on April 1, 2015. Our panel of machine learning scholars and researchers will formulate the question and choose the top three entries as the finalists, with the winner receiving $100,000 and a place on our podium at Machine Learning World 2017.
The challenge consists of 20 multiple-choice questions, all asking candidates to provide a generalization from the supervised learning process, saying what is the most helpful initial data, how to build a data collection element, what type of user would be best served by our approach, and much more.
You can find the full list of questions and the answers below. Further detail on the team’s choice of questions and the answers is provided below the fold.
The questions were inspired by a paper “The Deep Learning Book” by DeepMind Technologies, John Grzoska, David Ferrucci, John Joftes and Roman Wills.
We were just getting started to think about the questions when we got an email about 10 minutes before the quiz that was as surprising as it was unexpected.
“The Grand Challenge. Future manufacturing. Build a robot that can synthesize an original work of Picasso, Cézanne, or Bach.”
“The challenges are more important than the answers.”
   Machine Learning University  
1. A robot is controlled by a human using a combination of gestures, motions, and voice commands. Which of the following is NOT used for machine learning tasks?
a. Wild card
b. Causative variable
c. Adjustment
d. Solomon’s wisdom
2.A robot is controlled by a person with limited spatial cognition.The robot can only manipulate items in a rectangular array.The robot can rotate a three-dimensional object like a cube,but not a two-dimensional object.How would you use this confusion to your advantage?a.Compete at the Olympics
b.Sort cards in a deck
c.Navigate city traffic
3.A medical diagnosis requires the tracking of multiple factors such as age, weight, height, blood sugar, addition to identifying a disease.How would you use a combination of these factors to correctly diagnose a patient?
4.Given an ImageData of length L, produce an image file of length N including all of the pixels but excluding the pixels that are in the ImageData.
a. remaining data
b. duplicate data
5.Data cannot be changed after it has been accumulated.What is the best way to deal with this limitation?
6.Data must be able to be garbage collected.What is the difference between final,minimally optimal, and maximum optimizing conditions?
a. final minimally optimal = lowest measurement error
b. minimally optimal = lowest number of measurements
c. best=biggest improvement in accuracy
7.Given a set of attributes e1, e2, …, em, tell me how to calculate the total score from p1 to pN.
8.Given a sequence of measurements m1, …, mN, predict the probability that the next measurement will be made from one observation.a. make predictions for the ratios ‘equal’, ‘too small’, and ‘too large’
b. make predictions for the difference between ‘1’ and ‘0.5’
d. predict probability ‘0’ when nothing is changed
9.Define loss in terms of the probability of success for the specific task.
a. loss of the minority classifier
b. loss of 50% of the minority classifier
c. loss of this margin of error
10.Loss in percentage of test subjects who got all the answers right.
a. performance curve for New York Yankees
b. performance of American Idol
c. number of wrong answers in human knowledge<|endoftext|>This game is no longer supported! Click here to see the latest version of Wordament Game Lab 3.4

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