Federated learning is a distributed approach to machine learning that allows multiple parties to

collaboratively train a model without sharing their data with each other or a central server. This

protects the privacy of the user’s data while still enabling well-trained models that can benefit from

diverse and large-scale datasets. Reference: [Federated Learning - Wikipedia], [Federated Learning

for Mobile Keyboard Prediction - Google AI Blog]

Lasso regression is a type of linear regression that adds a regularization term to the loss function to

reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization

term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of

the coefficients to zero, which effectively eliminates some of the features from the model. Lasso

regression is preferable over ridge regression when there are many features with no association with

the dependent variable, as it can perform feature selection and reduce the complexity and noise of

the model.

Reinforcement learning is a type of machine learning that involves learning from trial and error

based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic

pathing, which is the problem of finding an optimal path from one point to another in an uncertain

and changing environment. Reinforcement learning can enable the model to adapt to new situations

and learn from its own actions and feedback. For example, a self-driving car company can use

reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner

should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR

applies to any organization that processes personal data of individuals in the European Union (EU),

regardless of where the organization is located. The GDPR grants individuals rights over their

personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR

also imposes obligations on organizations that process personal data, such as the duty to obtain

consent, conduct data protection impact assessments, implement data protection by design and by

default, and ensure accountability and transparency. The GDPR also addresses some specific issues

related to AI, such as automated decision-making, profiling, and data portability.

Sampling bias occurs when a data segment is collected in such a way that some members of the

intended statistical population are less likely to be included than others. This can result in a sample

that is not representative of the population and may lead to inaccurate or misleading conclusions.

Sampling bias can be caused by various factors, such as non-random sampling methods, non-

response, self-selection, or convenience sampling. Reference: [Sampling bias - Wikipedia], [What is

Sampling Bias? Definition, Types and Examples]