ML, Cloud computing and Edge computing Flashcards
Data mining (DM)
Focus on discovering patterns and knowledge from historical data to find valuable insights.
Machine learning (ML)
The ability of an algorithm to learn from data without being explicitly programmed for specific tasks.
Artificial Intelligence (AI)
Technologies and systems that act and think in a way similar to human behaviour, with the ability to make rational decisions.
Different types of machine learning algorithms(/types?)
Supervised machine learning, unsupervised machine learning and reinforcement learning.
Supervised machine learning
Classification: is when the output variable is a category, such as red or blue or disease and no disease.
Regression: a problem occurs when the output variable is a real value such as weight or price, e.g., house selling, stocks.
Algorithms for classification and regression in supervised machine learning?
Logistic regression: used to predict a binary outcome: yes/no, pass/fail.
Naive Bauers: calculate the possibility of whether a sample X belongs within a certain category or does not.
K-nearest neighbour: utilising training data to find the K closest relatives in future examples.
Decision tree: builds tree branches in a hierarchy approach, and each branch can be considered as an if-else statement.
Linear regression: a technique to examine whether there is a statistical relationship between a response variable and two or more explanatory variables (X).
Unsupervised machine learning
Splits datasets based on common attributes, detects anomalies that do not fit in any group and simplifies data by reducing dimensions. Clustering, anomaly detection and dimensionality reduction are common techniques. E.g., IoT in agriculture to segment plants based on sensor information.
Reinforcement learning
the machine is given feedback concerning the decision it makes, but no information about the possible alternatives. E.g., used in automated cars, and different games.
Challenges about ML in general
- Insufficient data: ML algorithms need a lot of data to work properly, simple problems may take thousands of examples and complex problems (image/speech recognition) may require millions.
- Poor quality data: if the training data is full of errors, outliners, and noises it will be hard for the system to detect underlying patterns.
- Irrelevant features: a critical part is coming up with a good set of features to train on.
- Feature selection: select the most useful features to train among existing features.
- Feature extraction: combining existing features to produce more useful ones, dimensionality reduction algorithms can help.
- Feature engineering: new features by gathering new data.
What is the role of ML in iot?
Predictive maintenance: finding signs of issues before a breakdown happens.
Anomaly detection: involves identifying events of data points that are outside the expected range.
Personalization: based on user behaviour and preferences, ml can be used to customize iot apps.
Environmental monitoring: data from sensors estimate environmental factors.
Resource optimization: ml can used to maximize the usage of resources like water, electricity and materials.
Smart transportation: cars, aeroplanes.
Challenges of using ML in IoT
What are some challenges in using ML in IoT?
Data quality: ML algorithms require high-quality data to provide accurate predictions.
Scalability: iot applications involve large amounts of data and a large number of devices, which can make it difficult to scale ml algorithms.
Latency: real-time or near-real-time decision-making is crucial.
Interoperability: ml algorithms may be challenging to integrate into iot devices and systems because they are frequently created using various technologies and standards.
Energy efficiency: iot devices often have limited power and processing resources, which can make it difficult to run complete machine learning algorithms.
Security: iot devices could be exposed to security risks like viruses or hacking.
What is the role of cloud computing in IoT?
cloud computing and iot complement each other. Cloud computing allows iot devices to record, capture, process and store data at a massive scale.
What are the benefits and limitations of edge computing?
by edge computing, we can better control data, reduce cost, provide faster insights and actions, and enable more continuous and streamlined operations. A very good option for sensitive areas - sensitive data. The limitation with edge is that large, complex models can not be deployed to edge service.
Edge vs cloud
Cloud Computing uses centralized servers in large, remote data centers to process and analyze data. It is best for applications that don’t need instant responses.
Edge Computing is a distributed system closer to users and devices. It processes data locally and analyzes it in real-time, making it ideal for situations where low latency is important and every millisecond counts.
Summary:
• Cloud: Good for non-time-sensitive tasks, data stored remotely. • Edge: Best for real-time tasks, data processed locally.
Cloud computing
Cloud computing is a model that provides users with easy and immediate access to shared resources via the internet. These resources can include networks, servers, storage and applications. Users can quickly access and configure these resources with minimal management and without needing much interaction with the service provider. Instead of saving files on a dedicated hard disk or local storage device, cloud-based storage makes it possible to save remotely. Five key characteristics of cloud computing are, On-Demand Self-Service, Ubiquitous Network Access, Resource Pooling, Rapid Elasticity and Measured Service.
