Overview
10 True/False: 4 points each
1) The definition of implementation is straightforward because implementation is a simple, direct process with defined boundaries.
2) One of the four components of BI systems, business performance management, is a collection of source data in the data warehouse.
3) A well-known early application of artificial intelligence was the chess program hosted at IBM's supercomputer (Deep Blue).
4) Data mining requires specialized data analysts to ask ad hoc questions and obtain answers quickly from the system.
5) Data that is collected, stored, and analyzed in data mining is often private and personal. There is no way to maintain individuals' privacy other than being very careful about physical data security.
6) Naïve Bayes is a simple probability-based classification method derived from the Bayes theorem.
7) Model ensembles tend to be more robust against outliers and noise in the data set than individual models.
8) Neural networks are called "black boxes" due to the lack of ability to explain their reasoning.
9) CNNs are only applicable to image data sets.
10) In Caffe, everything is done using text files instead of code.
10 Multiple choice: 8points each
11) What type of external decision-making factor are related to a country's market growth and financial conditions?
- A) political factors
- B) economic factors
- C) sociological and psychological factors
- D) environmental factors
12) What is the conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach?
- A) problem classification
- B) problem decomposition
- C) problem ownership
- D) problem design
13) Which of the following is not a technology or application of AI?
- A) speech understanding
- B) linguistics
- C) machine learning
- D) smart homes
14) This assumption in a regression analysis states that the explanatory variables are not correlated.
- A) linearity
- B) multicollinearity
- C) independence
- D) normality
15) What is the main reason parallel processing is sometimes used for data mining?
- A) because the hardware exists in most organizations and it is available to use
- B) because the most of the algorithms used for data mining require it
- C) because of the massive data amounts and search efforts involved
- D) because any strategic application requires parallel processing
16) Which data mining process/methodology is thought to be the most comprehensive, according to kdnuggets.com rankings?
- A) SEMMA
- B) proprietary organizational methodologies
- C) KDD Process
- D) CRISP-DM
17) Which element in an artificial neural network roughly corresponds to a synapse in a human brain?
- A) node
- B) input
- C) output
- D) weight
18) Why is sensitivity analysis frequently used for artificial neural networks?
- A) because it is required by all major artificial neural networks
- B) because it is generally informative, and can be used to identify a preferred model if multiple models exist
- C) because it is generally informative, although it cannot help to identify cause-and-effect relationships among variables
- D) because it provides a complete description of the inner workings of the artificial neural network
19) Which type of learning system is represented by the following process: input - manually created representation - output?
- A) knowledge-based systems
- B) classic machine learning
- C) generic representation learning
- D) deep learning
20) Which of the following does not describe a deep feedforward network?
- A) most general type of deep network
- B) many layers of neurons
- C) network elements are significantly different than ANN models
- D) tensors are handled as input
5 Essay Format: 16 points each
21) List some of the issues involved in data collection.
22) How does cognitive computing differ from artificial intelligence?
23) List the four steps of data preprocessing and include examples of activities that may be completed at each step.
24) Describe the difference between quantitative and qualitative data. Include definitions for nominal and ordinal data.
25) Describe the k-nearest neighbor (kNN) data mining algorithm.