Week 6 Assignment Complete: The Following Assignment In One ✓ Solved

Week 6 Assignmentcomplete The Following Assignment In One Ms Word Docu

Chapter 6– discussion question #1-5 & exercise 4 Questions for Discussion 1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot? 2. List and briefly explain different learning paradigms/methods in AI. 3. What is representation learning, and how does it relate to machine learning and deep learning? 4. List and briefly describe the most commonly used ANN activation functions. 5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN. Exercise 4. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.

Sample Paper For Above instruction

Deep learning has revolutionized the field of artificial intelligence (AI) by enabling computers to automatically learn complex patterns from vast amounts of data. Unlike traditional machine learning methods that often rely on manual feature extraction and shallow models, deep learning employs multi-layered neural networks capable of modeling high-level abstractions, thereby outperforming conventional techniques in tasks such as image recognition, natural language processing, and speech recognition (LeCun, Bengio, & Hinton, 2015). This advancement allows machines to improve their performance through end-to-end learning, reducing the dependency on handcrafted features and domain-specific knowledge.

Within AI, various learning paradigms or methods exist, each suited for different types of problems. Supervised learning involves training models on labeled datasets, enabling them to predict outcomes or classify inputs based on known outputs. Unsupervised learning, on the other hand, deals with unlabeled data, aiming to discover inherent structures or patterns, such as clustering or dimensionality reduction. Reinforcement learning teaches agents through trial-and-error interactions with the environment, where actions are rewarded or penalized to maximize cumulative reward (Sutton & Barto, 2018). Semi-supervised and self-supervised learning are hybrid approaches that leverage limited labeled data along with large volumes of unlabeled data, which are increasingly relevant in deep learning applications.

Representation learning is a core concept in modern machine learning and deep learning, referring to techniques that automatically discover the representations or features from raw data that make subsequent learning tasks more effective. It relates closely to feature extraction strategies, but with the advantage of enabling models to learn directly from raw inputs, such as pixel data in images or raw audio signals (Bengio, Courville, & Vincent, 2013). Deep learning excels at hierarchical representation learning, where lower layers capture simple features, and higher layers combine these into more abstract concepts, leading to more robust and generalizable models.

Artificial Neural Networks (ANNs), modeled after biological brains, use activation functions to introduce non-linearity, enabling them to learn complex functions. The most commonly used activation functions include sigmoid, hyperbolic tangent (tanh), ReLU (Rectified Linear Unit), Leaky ReLU, and softmax. The sigmoid function outputs a value between 0 and 1, making it suitable for binary classification. Tanh provides outputs between -1 and 1, often leading to faster convergence. ReLU, defined as max(0, x), is preferred for deep networks due to its computational efficiency and reduced vanishing gradient problem. Softmax is often used in the output layer for multi-class classification tasks, converting raw scores into probabilities (Nair & Hinton, 2010).

Multilayer Perceptrons (MLPs) are feedforward neural networks consisting of an input layer, one or more hidden layers, and an output layer. They work by processing input features through weighted connections. Summation nodes compute the weighted sum of inputs, which is then passed through an activation function. The role of weights in MLPs is to determine the importance of each input feature, enabling the network to learn which features contribute most to the output (Rumelhart, Hinton, & Williams, 1986). During training, weights are iteratively adjusted through backpropagation to minimize prediction errors, allowing the MLP to learn complex mappings from inputs to outputs.

In recent years, cognitive computing has gained prominence due to its ability to simulate human thought processes and solve complex real-world problems. One notable example is IBM Watson, which gained fame by winning Jeopardy! in 2011, demonstrating natural language understanding and reasoning capabilities (Ferrucci et al., 2010). Watson’s success illustrated how cognitive systems can analyze unstructured data, learn from interactions, and provide insights across various domains such as healthcare, finance, and customer service.

Healthcare is a prime application area where cognitive computing has significantly improved patient outcomes. For instance, IBM Watson Oncology assists oncologists by analyzing patient records, medical literature, and clinical guidelines to recommend personalized cancer treatments. This system rapidly processes vast datasets, offering evidence-based treatment options while accounting for patient-specific factors, thus reducing diagnostic errors and enhancing treatment efficacy (Shah et al., 2019).

In the financial sector, cognitive computing enables advanced fraud detection and risk management. Systems analyze transactional data, identify anomalies, and predict fraudulent activities in real-time, significantly reducing financial losses. They also assess credit risk by integrating diverse data sources, including social media and behavioral patterns, to make more accurate lending decisions (Foutas et al., 2018).

Another critical application is in customer service, where virtual assistants and chatbots powered by cognitive computing provide 24/7 support, resolve complex queries, and learn from interactions to improve over time. These systems utilize natural language processing to understand context, sentiment, and intent, thereby delivering personalized and efficient customer experiences (Adamopoulou & Mousavi, 2020). The versatility and scalability of cognitive systems continue to expand, demonstrating their vital role in solving complex real-world challenges across sectors.

References

• Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
• Foutas, A., et al. (2018). Cognitive analytics in financial risk management. Journal of Financial Data Science, 1(2), 45-60.
• Ferrucci, D., et al. (2010). Building Watson: An overview of the DeepQA project. AI Magazine, 31(3), 59-79.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
• Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, 807-814.
• Shah, S. M. A., et al. (2019). Application of IBM Watson Oncology for personalized cancer treatment. Journal of Medical Systems, 43, 120.
• Adamopoulou, E., & Mousavi, M. (2020). Chatbots: What is their future? Proceedings of the 15th International Conference on Advances in ICT for Emerging Regions, 273-278.