Chapter 9 Flashcards
What are some marketing campaigns and advertising tactics used by retailers?
Direct marketing emails and digital advertisements
These campaigns often include incentives or discounts based on customer demographics.
What is the importance of effectively targeting customers in marketing campaigns?
Achieving a high conversion rate while minimizing advertising costs and reducing customer disturbances.
What do ML models optimize in marketing campaigns?
The effectiveness of marketing campaigns by identifying potential customers and determining appropriate messaging and incentives.
What is a traditional method for understanding different customer segments?
Segmentation using basic demographic data.
What is highly personalized marketing?
Creating accurate individual profiles using large amounts of individual behavior data.
Fill in the blank: Highly personalized campaigns can be generated using _______.
[individual profiles]
What does contextual advertising involve?
Displaying ads relevant to the content on a web page.
How can ML assist with contextual advertising?
By identifying the context of an ad to ensure appropriate placement.
What is generative AI’s role in targeted marketing?
Creating dynamically personalized content tailored to individual customer preferences.
Why is understanding consumer perception crucial for retail businesses?
It significantly impacts their success and brand reputation.
What techniques do retailers use to assess customer sentiment?
Soliciting feedback and monitoring social media channels.
What is sentiment analysis?
A text classification problem to determine whether sentiment is positive, negative, or neutral.
What can ML algorithms do in sentiment analysis?
Train models to detect sentiment in text data.
What is the purpose of inventory planning and demand forecasting?
To manage inventory costs while maximizing revenue and avoiding out-of-stock situations.
What are traditional methods for demand forecasting?
Buyer surveys, expert opinions, and projections based on past demands.
What techniques are retailers using to improve demand forecasting?
Statistical and ML techniques such as regression analysis and deep learning.
What does a deep learning model do in demand forecasting?
Recognizes patterns and relationships in data to generate accurate forecasts.
What types of forecasts can ML-based forecasting models generate?
Point forecasts and probabilistic forecasts.
What role does AI and ML play in the automotive industry?
Improving efficiency, safety, and customer experience.
What is a significant application of AI and ML in the automotive industry?
Autonomous driving.
What are the three main stages of the system architecture of an autonomous vehicle?
Perception and localization, decision and planning, control.
What is the perception stage in autonomous driving?
Gathering information about surroundings through sensors.
What components do autonomous vehicles use in the perception stage?
RADAR, LIDAR, cameras, and recognition systems.
What is the function of the decision and planning stage in autonomous vehicles?
Controls the motion and behavior based on data collected.
How does AI/ML enhance the path planning process in autonomous vehicles?
By analyzing real-time map information, traffic patterns, and user inputs.
What is the role of the control module in autonomous driving?
Translates decisions into physical actions that control the vehicle.
How can reinforcement learning be used in the control module of autonomous vehicles?
To learn optimal control policies through trial and error.
What does ADAS stand for?
Advanced Driver Assistance Systems.
What are some features of ADAS?
Lane departure warning systems and automatic emergency braking systems.
What is the significance of understanding ML algorithms for ML solutions architects?
To identify suitable data science solutions and design effective technology infrastructure.
What is an objective function in ML?
A business metric used for optimization, such as the disparity between projected and actual sales.
What optimization technique is widely used in ML?
Gradient descent.
Fill in the blank: The learning rate controls the magnitude of parameter updates at each _______.
[iteration]
What is gradient descent?
An iterative approach for optimizing neural networks and ML algorithms by calculating the rate of error change (gradient) associated with input variables.
What does the learning rate control in gradient descent?
The magnitude of parameter updates at each iteration.
What are the key steps in the gradient descent optimization process?
- Initialize the value of W randomly.
- Calculate the error (loss).
- Compute the gradient of the error with respect to the loss function.
- Update the value of W to reduce the error.
- Repeat until the gradient becomes zero.
What is the normal equation in the context of ML?
A one-step analytical solution for calculating the coefficients of linear regression models.
What is overfitting in machine learning?
When a trained model learns the training data too well but fails to generalize to new, unseen data.
What types of problems are classification algorithms suited for?
Tasks where the goal is to categorize data into distinct classes.
What are key factors to consider when selecting a ML algorithm?
- Problem type
- Dataset size
- Number and nature of features
- Computational requirements
- Interpretability of results
- Assumptions about data distribution
What is classification in machine learning?
A task that assigns categories or classes to data points.
What is regression in machine learning?
A technique used to predict continuous numeric values.
What is linear regression?
A method that utilizes a linear function of coefficients and input variables to predict a scalar output.
What is the primary goal of logistic regression?
To find a decision boundary that separates two classes of data points.
How does logistic regression ensure predicted outputs fall within a specific range?
By applying a logistic function to the linear combination of input variables.
What is a decision tree?
A hierarchical model that splits input data based on rules to make predictions.
What algorithms are used to determine how to split a decision tree?
- Gini purity index
- Information gain
What is a key advantage of decision trees over linear models?
Their ability to capture non-linear relationships and interactions between features.
What is a limitation of decision trees?
They can be sensitive to outliers and prone to overfitting.
What is a random forest algorithm?
An ensemble method that combines the decisions of multiple decision trees to improve performance.
How does a random forest make decisions?
By creating multiple smaller trees and combining their outputs through majority voting or averaging.
What are some advantages of random forests?
- Improved accuracy
- Reduced overfitting
- Robust to outliers
- Feature importance estimation
What is gradient boosting?
A sequential approach that aggregates results from different trees, where each tree corrects the errors of the previous one.
What are some advantages of gradient boosting?
- Excels with imbalanced datasets
- Potential for higher performance when tuned
- Supports custom loss functions
- Captures complex relationships
What is a limitation of gradient boosting?
It lacks parallelization capabilities, making it slower in training compared to parallelizable algorithms.
What is the primary advantage of gradient boosting?
It has the potential to achieve higher performance than other algorithms when properly tuned.
What flexibility does gradient boosting offer in modeling?
It supports custom loss functions.
How does gradient boosting perform in terms of capturing data relationships?
It can effectively capture complex relationships in the data and produce accurate predictions.
What is a limitation of gradient boosting related to training speed?
Due to its sequential nature, it lacks parallelization capabilities, making it slower in training.
How does gradient boosting respond to noisy data?
It is sensitive to noisy data, including outliers, which can lead to overfitting.
What is a challenge of interpreting gradient boosting models?
The complexity of gradient boosting models can make them less interpretable compared to simpler algorithms.
What is XGBoost?
A widely-used implementation of gradient boosting that offers several improvements.
How does XGBoost improve training times?
It enables training a single tree across multiple cores and CPUs.
What techniques does XGBoost incorporate to enhance model performance?
Powerful regularization techniques to mitigate overfitting and reduce model complexity.
What type of datasets does XGBoost excel in handling?
Sparse datasets.
Name two other popular variations of gradient boosting trees.
- LightGBM
- CatBoost
What types of tasks is K-NN used for?
Both classification and regression tasks.
What assumption underlies the K-NN algorithm?
Similar items tend to have close proximity to each other in the feature space.
What metric is often used to measure distances in K-NN?
Euclidean distance.
How is the class label determined in K-NN classification?
Through majority voting among the K nearest neighbors.
Fill in the blank: The K nearest neighbors to the new data point are identified by calculating their ______.
distances to the existing data points.
