Interpretable AI Building Explainable Machine Learning Systems 1st Edition Ajay Thampi - Solutions

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=+2. Explain the levels of language understanding.
=+1. Name and explain different areas of NLP.
=+5. Explain semantic autosuggest. How can it be implemented?
=+4. When to use which technology?
=+3. Name state-of-the-art information retrieval tools and technologies.
=+2. What are the main services of information retrieval tools?
=+1. What does the term information retrieval mean?
=+7. Which programming language should be used for developing an AI application?
=+6. In which situations is the use of an agent framework recommended? In which not?
=+5. Which services do agent frameworks offer?
=+4. What is an agent? Give examples.
=+machine learning framework, agent framework?
=+3. Could you speak of an AI application if none of these technologies are used: reasoning engine,
=+2. What are the main components of the AI reference architecture?
=+1. What characterizes an AI application?
=+20. How to select knowledge representation products for a concrete AI application project?
=+19. How to integrate various off-the-shelf knowledge graphs?
=+18. How to select knowledge graphs for a concrete application use cases?
=+17. Name a few products that implement those services (product map).
=+16. Name knowledge representation services (services map).
=+15. How can rules be implemented with SPARQL?
=+14. What advanced features does SPARQL offer?
=+13. How is the result set of SPARQL queries structured?
=+12. How are simple queries structured in SPARQL?
=+11. What is linked data? Give examples of off-the-shelf knowledge graphs.
=+10. Which serialization syntaxes exist for RDF?
=+9. How are properties declared in RDF? How are properties used?
=+8. How are classes declared in RDF? How are individuals (instances) assigned to classes?
=+7. What is a resource in RDF? How are namespaces used? What is an RDF triple?
=+What are implications of the assumption made?
=+6. Explain the open world assumption and closed world assumption. What are the differences?
=+5. What does reasoning mean? Give an example. How does it work?
=+4. Name different knowledge representation approaches.
=+3. What is a knowledge graph?
=+2. Give examples for classes, individuals, relationships, and rules.
=+1. What is the purpose of knowledge representation?
=+• How to answer questions, e.g., “Which Renaissance artists lived in Italy?”
=+• How to reason over knowledge, e.g., “Someone who has created paintings is an artist”
=+• How to represent knowledge, e.g., “Michelangelo is a Renaissance artist”
=+19. What are sources of bias in ML applications and how to deal with them?
=+18. How to proceed in engineering an ML application (methodology)?
=+17. Name prominent products for each ML service
=+16. Explain the main services of ML
=+15. Explain the concepts of services maps and product maps. How can they be used for selecting products in a project?
=+14. How does learning curve analysis work for classification (instead of regression)?
=+13. How to increase / decrease model complexity?
=+12. What are learning curves? How to interpret them?
=+11. Explain overfitting and underfitting
=+10. What is bias and variance?
=+9. Explain k-fold cross-validation
=+8. Explain the measures accuracy, precision, recall, F1 score, MAE, RSME and MSE
=+7. Explain the process for supervised ML. How to use training data and test data?
=+networks, deep learning. Name other approaches.
=+6. Explain the following ML approaches: decision tree learning, Artificial Neural Networks, Bayes
=+5. Explain the following ML tasks: classification, regression, clustering, feature selection / extraction, topic modeling
=+4. Explain the following ML areas: supervised learning, unsupervised learning, reinforcement learning.
=+3. Name applications of ML.
=+2. When to use ML / when not to use ML?
=+1. What is ML?
=+When to use ML?
=+What does ML mean?
=+6. Name a few prominent AI projects
=+5. What are potential impacts of AI applications on society?
=+4. In what sense is AI today relevant and ubiquitous?
=+3. Sketch the history of AI
=+2. What are main areas of AI?
=+1. What does the term “Artificial Intelligence” mean?
=+ What interpretability is and its importance
==+ How machine learning systems are built
Learning dense word representations that capture semantic meaning
Using counterfactual examples to come up with contrastive explanations
=+ A summary of interpretability techniques covered in this book
=+ How interpretable machine learning systems are built
=+ Different types of machine learning systems1===+ How machine learning systems are built1===+ What interpretability is and its importance
=+system and applying interpretability techniques to answer them1===+ Using counterfactual examples to come up with contrastive explanations
=+ Common questions asked of an explainable AI
=+ Understanding the properties of an explainable AI system
=+ A recap of interpretability techniques learned in this book
=+improve transparency and accountability and to ensure compliance with regulation
=+ Documenting datasets using datasheets to
=+ Mitigating bias using preprocessing techniques
=+source of discrimination in machine learning models
=+ Applying interpretability techniques to identify the
=+fair using various fairness notions
=+ Validating whether machine learning models are
=+ Identifying sources of bias in datasets
=+ Validating visualizations generated by PCA and t-SNE qualitatively and quantitatively
=+ Strengths and weaknesses of PCA and t-SNE
=+dimensionality-reduction techniques like PCA and t-SNE
=+ Visualizing semantic similarity of highdimensional word embeddings using
=+ Does the CNN identify any features or concepts that are understandable by humans?1===+ Learning dense word representations that capture semantic meaning
=+ How does the CNN decompose the task of understanding an image?
=+ Strengths and weaknesses of the network dissection framework
=+in the convolutional neural network and how to visualize them
=+ Quantifying the interpretability of layers and units
=+ Running the network dissection framework
=+that are learned by the layers and units
=+network to understand the features or concepts
=+ Dissecting a black-box convolutional neural
=+mapping techniques and how to perform sanity checks on them

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Interpretable AI Building Explainable Machine Learning Systems 1st Edition Ajay Thampi - Solutions

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