Machine Learning Assignment | Sample#02

1. List some key issues raised in each video?

Some major factors which are raised by different speakers are:

1.    Modern networks are complex and constantly produce too much data to consume.

2.    Machine learning requires a large amount of data.

3. Telecom has major challenges and transformation.

4. Machine learning does not validate when data is biased.Then, we take terrible decisions.

5. Social scientists might start relying on machine learning a lot more than they should. But we still need a lot of improvements in unsupervised learning.

6. What is learned is not easy to understand.

2. What is the reason for telecom companies to adapt Machine Learning (besides profit)?

Ultimately, Artificial Intelligence and Machine Learning have enabled the telecom industry to extract insights from their vast data sets, made it easier to resolve issues, manage daily business efficiently and provide improved satisfaction and customer service.

As machine learning becomes ubiquitous, we will soon be hard-pressed to find any industry not capitalizing on the benefits they can provide. Telecom is one of the fastest-growing industries as well as one that uses artificial intelligence and ML in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability and customer’s satisfaction. Telecom uses machine learning by using virtual assistants and chatbots, Virtual assistants automate and scale responses to the support requests for set up.

3. Categorize different ML models and describe how they fit in the end to end cloud architecture?

Machine learning shows a large potential for enhancing the design process since some of its learning algorithms resemble reasoning processes that are frequently applied by architects, such as abductive reasoning, a form of logical inference based on observation and infernal of the most likely explanation for what has been observed.

Abductive reasoning in design explains the frequent practice of making the designs before their implementation, i.e. it may be based on past observations and experiences, architects select whatever elements fulfil the initial representation. Machine learning also impact on architecture through (1)conceptualization, including conceptual definition, approach, and explanation given by the designer,(2)algorithmization, which consists of developing and implementing a a program capable of representing and instantiating the applied concepts,(3) modelling, which does the task of 3D modelling either for visualization or building simulation and (4)optimization,i.e the search for the high performance solutions according to different criteria defined beforehand by the design team. All of these are connected with each other in performance.

4. Argue that the ML approach must be rearchitected to take into account the notion of multi tending in multi-layer networks: Why?

Machine learning enables a system to scrutinize data and deduce knowledge. It goes beyond simply learning or extracting knowledge. It also goes beyond simply learning and utilizing knowledge over time and with experience. The main goal of Machine learning is to identify and exploit hidden patterns in “training” data.

As machine-learning analyzes unknown data, such that it can be grouped together or mapped to the known group. That’s why the rechecking of machine learning is highly recommended to take into account the notion of multi tending in multi-layer networks.

Reference : Sample#2