1. Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe com- monalities and differences among their findings and prepare a report to summarize your understanding.

2. What is an artificial neural network and for what types of problems can it be used?

3. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by arti- ficial ones? What aspects are similar?

4. What are the most common ANN architectures? For what types of problems can they be used?

5. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode

question 1 is limit to 1-2 pages. questions from 2-5 can be on 2 pages. all 6 question should be in one doc with APA formating.

Answer preview

…Machine learning methods have been applied in various fields, such as solving problem domains in healthcare. Hence, with these methods, they predict risk factors through flexible techniques and algorithms, thus informing experts on areas that need improvement. Various studies have been conducted, which compare the applicability of multiple machine-learning methods. For instance, there is deep learning, which involves the use of artificial neuron networks. Other learning models incorporate different algorithms like support vector machines (SVM) or networks, Bayesian networks, K-nearest neighbors (kNN), random forests (RF), among others. In the study by Khondoker, Dobson, Skirrow, Simmons, and Stahl (2016), they purposed at comparing the performance of machine learning techniques to predict clinical decadence in hospital wards. They compared the classical technique that applies the Linear Discriminant Analysis (LDA) algorithms, and the modern methods support vector machines, which use models like the SVM training algorithm. From their findings, Khondoker ey al. (2016) discovered that the machine learning approach that incorporates LDA was more strategic in a smaller sample size. However, the SVM outplayed the LDA and performed exceptionally when compared with RF and kNN…

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