Category: Artificial Intelligence

Read the Uber case and other readings (linked in the syllabus) for the session and answer the following questions:
1) Develop a list of hypotheses Uber could use to predict a rider’s pickup location with information such as the rider’s previous trips and current destination, as well as historical patterns related to the pickup location. Augment the case information with your personal Uber experiences to suggest potential hypotheses.
2) Create a quantitative pickup quality metric using attributes derived from the passive, active and third-party signals available to Uber. Discuss why your selected attributes represent a robust pickup quality metric. What weights would you assign to the features you choose for your pickup model?
3) Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?
4) Discuss the steps involved in setting up an ML model for automating pickups at scale. Use the framework of the seven-step model in the case (Exhibit 7) to elaborate on how Uber should apply this framework to the ML model. Hint: you can create a table and list the tasks under each step.
5) Is there a role for unstructured data for the issues we have examined in this case? For which prediction problem is it likely to make the biggest impact and for which is it likely to make the least impact? Explain why.
6) Where is human domain knowledge likely to be helpful for the prediction problems above? Where do you think such knowledge is unlikely to be helpful? Explain.

This exercise asks you to implement a simple deep learning:
Textbook Reference: Artificial Intellegence: A Modern Approach – Section 21
Implement a data structure for general computation graphs, (e.g. as described in Section 21.1) and define the node types required to support feed-forward neural networks with a variety of activation functions.
Write a function that computes the outputs of the computation graph given the inputs.
Now write a function that computes the error for a labelled example.
Finally, implement the local back-propagation algorithm based on Equations (21.11) and (21.12) and use it to create a stochastic gradient descent learning algorithm for a set of examples.

Assignment due Sunday, March 17, 2024 by 11:00pm
Answer the following questions. You have to upload a PDF file as the primary resource. You can upload any additional file as a secondary resource. Please note that you need to provide clear and detailed explanations for all the solutions that you provide.
Answer the following questions. Upload your answers in a pdf file.
Question 1
KB entails a sentence α (KB |= α) if and only if, in every model KB is true, α is true as well. M (KB) is a subset of M(α). One way to implement the inference is to enumerate all the models and check that α is true in every model that KB is true.
Assume a simplified version of the problem with breezes and pits. Squares next to pits are breezy, and breezy squares are next to squares with pits.
The agent did not detect a breeze at square [1,1] (column, row). The agent detected a Breeze in [2,1]. Thus, your knowledge base is KB : (¬ B1,1) ∧ (B2,1), where Bx,y is true if there is a breeze in [x,y].
Below you can see all possible models of adjacent pits: A pit is represented as a black cell.
1.1. Surround with a line the possible worlds above that are models of KB
1.2. Consider the sentence α1 = “Square [1,2] does not have a pit.” Surround with a line the possible worlds below that are models of α1.
1.3. Does KB |= α1? Explain your answer
1.4. Consider the sentence α2 = “Square [2,2] does not have a pit.” Surround with a line the possible worlds below that are models of α2.
Question 2:
Assume that you are given the following configuration. Compute the probability P3,1. Each square other than [1,1] contains a pit with a probability of 0.3.
Hint: Use section 12.7 for a similar example.
2.1 What is the evidence?
2.2. Write the formula for the full joint distribution. How many entries are there?
2.2 Use conditional independence to simplify the summation.
Question 3:
Given the network below, calculate marginal and conditional probabilities P (¬p3), P(p2|¬p3), P(p1|p2, ¬p3) a P(p1|¬p3, p4). Apply inference by enumeration. P(p1)=0.4 P(p2/p1)=0.8, P(p3/p2)=0.2 P(p3/¬p2)=0.3, P(p4/p2)=0.8, P(p4/¬p2)=0.5. Optional: Can you consider the case of using variable elimination?
Assignment Information
Weight:20%
Learning Outcomes Added
LO1_FundamentalsAI: Identify key concepts relating to various AI techniques.
LO2_ReasoningAI: Apply logic, probabilistic reasoning, and knowledge representation strategies in solving AI problems.
Above is the assignment requirements, please note that i have completed the assignment and the task i need you to complete is review everything and fix any mistakes.

Assignment due Sunday, March 17, 2024 by 11:00pm
Answer the following questions. You have to upload a PDF file as the primary resource. You can upload any additional file as a secondary resource. Please note that you need to provide clear and detailed explanations for all the solutions that you provide.
Answer the following questions. Upload your answers in a pdf file.
Question 1
KB entails a sentence α (KB |= α) if and only if, in every model KB is true, α is true as well. M (KB) is a subset of M(α). One way to implement the inference is to enumerate all the models and check that α is true in every model that KB is true.
Assume a simplified version of the problem with breezes and pits. Squares next to pits are breezy, and breezy squares are next to squares with pits.
The agent did not detect a breeze at square [1,1] (column, row). The agent detected a Breeze in [2,1]. Thus, your knowledge base is KB : (¬ B1,1) ∧ (B2,1), where Bx,y is true if there is a breeze in [x,y].
Below you can see all possible models of adjacent pits: A pit is represented as a black cell.
1.1. Surround with a line the possible worlds above that are models of KB
1.2. Consider the sentence α1 = “Square [1,2] does not have a pit.” Surround with a line the possible worlds below that are models of α1.
1.3. Does KB |= α1? Explain your answer
1.4. Consider the sentence α2 = “Square [2,2] does not have a pit.” Surround with a line the possible worlds below that are models of α2.
Question 2:
Assume that you are given the following configuration. Compute the probability P3,1. Each square other than [1,1] contains a pit with a probability of 0.3.
Hint: Use section 12.7 for a similar example.
2.1 What is the evidence?
2.2. Write the formula for the full joint distribution. How many entries are there?
2.2 Use conditional independence to simplify the summation.
Question 3:
Given the network below, calculate marginal and conditional probabilities P (¬p3), P(p2|¬p3), P(p1|p2, ¬p3) a P(p1|¬p3, p4). Apply inference by enumeration. P(p1)=0.4 P(p2/p1)=0.8, P(p3/p2)=0.2 P(p3/¬p2)=0.3, P(p4/p2)=0.8, P(p4/¬p2)=0.5. Optional: Can you consider the case of using variable elimination?
Assignment Information
Weight:20%
Learning Outcomes Added
LO1_FundamentalsAI: Identify key concepts relating to various AI techniques.
LO2_ReasoningAI: Apply logic, probabilistic reasoning, and knowledge representation strategies in solving AI problems.
Above is the assignment requirements, please note that i have completed the assignment and the task i need you to complete is review everything and fix any mistakes.

Q1 ) i. Write the SWOT analysis of a fast-food restaurant business.
ii. Identify key success factors and perform the competitive analysis.
Q2) i. Evaluate which business legal structure (partnership, sole trader, private limited, public limited, etc.) would suit Sarah best to establish her business. Justify your choice.
ii. Make sure to list the pros and cons about your choice.
Q3)a) Compare the pros and cons of the new franchise Cinnaholic and the established franchise Brioche Doree.
b) Based on comparison, from the perspective of the franchisees, what is the best offer and why?
Q4)a) Calculate earnings on tangible assets.
b) Calculate the value of the business using the excess earnings method (EEM).

Q1 ) Compare traditional and Enterprise Systems (ES) software implementation in term of:
Focus
Implementation Time
Cost
Example
Q2) Enterprise Architecture consisting of:
Business Architecture,
Information Architecture,
Application Architecture Technical Architecture. How can we apply them on the following example: Online store?
Q3)What does quality mean in general? There are two perspectives involved in quality. List them and provide an example for each one. Use your own words. Q4)In your own words, what do the following concepts mean? Support your answers with examples. – Time to market – Lifetime – Tradeoffs
– Stakeholders

Objective: The goal of this assignment is to gain hands-on experience in designing and implementing a deep neural network using the Keras library in Python. You will select a dataset of your choice from kaggle and apply a deep learning model to it. The focus is on experimenting with different activation functions and optimization techniques to understand their impact on the model’s performance.
Requirements:
Dataset Selection:Choose a dataset that interests you. It can be related to image classification, text analysis, or any other domain.
Perform necessary data preprocessing steps, such as normalization, encoding categorical variables, splitting into training and testing sets, etc.
Model Design:Design a deep neural network using Keras.
Your network should have at least 3 hidden layers.
Activation Functions:Experiment with three different activation functions: Sigmoid, Tanh, and ReLU.
Implement these activation functions in different models (or layers) to observe their effects on your network’s performance.
Optimization Technique:Use either Gradient Descent or Stochastic Gradient Descent (SGD) as your optimization technique.
Discuss how your chosen optimizer influences the training process and outcomes.
Evaluation:Evaluate the performance of your models using appropriate metrics (such as accuracy.).
Compare the results obtained with different activation functions and discuss your findings.
Report:Write a report documenting your data preprocessing steps, model architecture, training process, results, and analysis.
Include visualizations such as loss and accuracy curves, confusion matrices, etc., where applicable.
Provide a link to the dataset.
Submission:
Submit your Jupyter Notebook containing the code, along with the report. Submit a ipynb file.

Objective: The goal of this assignment is to gain hands-on experience in designing and implementing a deep neural network using the Keras library in Python. You will select a dataset of your choice from kaggle and apply a deep learning model to it. The focus is on experimenting with different activation functions and optimization techniques to understand their impact on the model’s performance.
Requirements:
Dataset Selection:Choose a dataset that interests you. It can be related to image classification, text analysis, or any other domain.
Perform necessary data preprocessing steps, such as normalization, encoding categorical variables, splitting into training and testing sets, etc.
Model Design:Design a deep neural network using Keras.
Your network should have at least 3 hidden layers.
Activation Functions:Experiment with three different activation functions: Sigmoid, Tanh, and ReLU.
Implement these activation functions in different models (or layers) to observe their effects on your network’s performance.
Optimization Technique:Use either Gradient Descent or Stochastic Gradient Descent (SGD) as your optimization technique.
Discuss how your chosen optimizer influences the training process and outcomes.
Evaluation:Evaluate the performance of your models using appropriate metrics (such as accuracy.).
Compare the results obtained with different activation functions and discuss your findings.
Report:Write a report documenting your data preprocessing steps, model architecture, training process, results, and analysis.
Include visualizations such as loss and accuracy curves, confusion matrices, etc., where applicable.
Provide a link to the dataset.
Submission:
Submit your Jupyter Notebook containing the code, along with the report. Submit a ipynb file.

I need to have a complete project, but it will be developed in three parts (1, 2 and the 3- final complete report). Please save each part in a separate file.There should be three files i.e.part 1, part 2 and part 3- Completion of Final Project Report.
Each part has instructions for it already, but I am looking for a part of the complete project written and not an outline.
Each part should be 3-4 pages in length not including the title page and reference list.
So, when later combining each part, the complete project should be 6-8 pages again not including the title page and reference list.

below I will attach my paper and feedback from my instructor please review the feedback and make the paper sufficient according to the feedback provided please