1. PCA can be used for projecting and visualizing data in lower dimensions.

• a. True

• b. False.

2. Which of the following is the best machine learning method?

• a. Scalable.

• b. Accuracy.

• c. Fast.

• d. All of the above.

3. What characterize unlabeled examples in machine learning?

• a. There is no prior knowledge.

• b. There is no confusing knowledge.

• c. There is prior knowledge.

• d. There is plenty of confusing knowledge.

4. What does dimensionality reduction reduce?

• a. Stochastics.

• b. Collinerity.

• c. Performance.

• d. Entropy.

5. Data used to build a data mining model is called ___________.

• a. Training data.

• b. Validation data

• c. Test data.

• d. Hidden data.

6. The problem of finding hidden structure is unlabeled data is called _________.

• a. Supervised learning.

• b. Unsupervised learning.

• c. Reinforcement learning.

• d. None of the above.

7. Of the Following Examples, Which would you address using an supervised learning Algorithm?

• a. Given email labelled as spam or not spam, learn a spam filter.

• b. Given a database of customer data, automatically discover market segments and group customers into different market segments.

• c. Find the patterns in market basket analysis.

• d. Given a set of news articles found on the web, group into some of articles about the same story.

8. Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model.

• a. True

• b. False.

9. You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of ___________.

• a. Supervised learning.

• b. Unsupervised learning.

• c. Semisupervised learning.

• d. Reinforcement learning.

10. Which of the following is a good test dataset characteristic?

• a. Large enough to yield meaningful results

• b. Is representative of the dataset as a whole.

• c. Both a and b.

• d. None of the above.