AI Course Suitability Assessment Survey
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1. Advanced AI Course Suitability Assessment Survey
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Step
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of 8
Student Name
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First
Last
Phone
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Email
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Stage 1: Foundational Information and Learning Objectives
1. What is your highest level of educational attainment? (Select one)
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Pre-high school graduation
Currently pursuing a bachelor's degree
Bachelor's degree holder
Currently pursuing or holding a master's degree
Currently pursuing or holding a doctoral degree
2. What is your current profession or field of study? (Select all that apply)
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Computer Science/Engineering
Mathematics/Statistics
Natural Sciences
Social Sciences
Business/Management
Other:___
Other:
3. What is your primary objective for AI study? (Select one)
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Fulfilling personal curiosity
Applying AI techniques to current professional role
Transitioning career into AI-related fields
Conducting academic research
Realizing entrepreneurial or business concepts
Other:___
Other
4. Please rate your preferred learning methodologies (Scale 1-5, 1: Least preferred, 5: Most preferred)
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1
2
3
4
5
Video lectures
1
Video lectures 1
2
Video lectures 2
3
Video lectures 3
4
Video lectures 4
5
Video lectures 5
Practical and project-centric learning
1
Practical and project-centric learning 1
2
Practical and project-centric learning 2
3
Practical and project-centric learning 3
4
Practical and project-centric learning 4
5
Practical and project-centric learning 5
Text-based learning
1
Text-based learning 1
2
Text-based learning 2
3
Text-based learning 3
4
Text-based learning 4
5
Text-based learning 5
Interactive/dialogic learning
1
Interactive/dialogic learning 1
2
Interactive/dialogic learning 2
3
Interactive/dialogic learning 3
4
Interactive/dialogic learning 4
5
Interactive/dialogic learning 5
Collaborative group learning
1
Collaborative group learning 1
2
Collaborative group learning 2
3
Collaborative group learning 3
4
Collaborative group learning 4
5
Collaborative group learning 5
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Stage 2: Technical Background Assessment
5. Evaluate your programming proficiency level:
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No experience
Rudimentary (capable of writing simple scripts)
Intermediate (comprehension and utilization of functions, classes, etc.)
Advanced (capable of designing and implementing complex programs)
Expert (experience with large-scale software projects)
6. Select all programming languages you are proficient in:
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Python
Java
C/C++
JavaScript
R
MATLAB
Other:___
Other:
7. Assess your mathematical background (Select all that apply):
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Fundamental Algebra
Linear Algebra
Calculus
Probability Theory and Statistics
Optimization Theory
Other:___
machine a in
Other:
8. If you have experience in data science or machine learning, please indicate (Select all that apply):
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Data analysis and visualization
Basic statistical modeling
Experience with machine learning algorithms
Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
Experience with big data processing tools (e.g., Hadoop, Spark)
No experience
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Stage 3: AI Knowledge and Areas of Interest
9. Evaluate your current level of AI knowledge:
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No knowledge
Familiar with basic concepts only
Understanding of some AI technologies
Comprehensive knowledge of major AI domains and technologies
AI expert level
10. Select all AI domains you are interested in (Not fixed, can be updated):
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Data Analysis Master Class
ADsP Certification Course
Excel Practice
Business Writing Course
ChatGPT Utilization
Backend Development
Front-End Web Development
Mobile App Development
Unity & VR/AR Development
DevOps and Infrastructure Management
SQL & Data Visualization Bundle
Data Visualization Techniques
Deep Learning Advanced
Recommendation Systems
Computer Vision
MLOps Pipeline
Marketing Content with AI
Report Planning for New Employees
Digital Literacy
Other:
11. If you have AI project experience, please indicate (Select all that apply):
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Completion of basic tutorials or online course projects
AI projects conducted in university courses
Professional application of AI technologies
Participation in personal or open-source AI projects
Publication of AI-related research papers
No experience
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Stage 4: Learning Environment and Constraints
12. How much time can you allocate to learning per week?
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Less than 5 hours
5-10 hours
11-20 hours
More than 20 hours
13. What is your preferred course duration?
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Short-term course (1-4 weeks)
Medium-term course (1-3 months)
Long-term course (3-6 months)
In-depth program (more than 6 months)
14. What is your budget range for course enrollment?
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Only considering free courses
Less than $100
$100 - $500
$500 - $1000
More than $1000
15. How important are certifications or degrees to you?
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Not important at all
Somewhat important
Important
Very important
16. What are your primary obstacles to learning? (Select all that apply)
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Time constraints
Financial limitations
Insufficient technical background
Limited English proficiency
Difficulty in maintaining motivation
Lack of practical environment (hardware/software)
Other:___
Other:
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Stage 5: Additional Information
17. If you have specific goals or projects you wish to achieve through AI learning, please briefly describe:
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18. If you have any additional considerations or information to provide, please elaborate:
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Stage 6: Precision AI Knowledge Assessment
19. Which of the following best describes the relationship between AI, Machine Learning, and Deep Learning?
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They are completely separate fields
AI is a subset of Machine Learning
Machine Learning is a subset of AI, and Deep Learning is a subset of Machine Learning
Deep Learning is a superset of AI
I'm not sure
20. What is the primary difference between supervised and unsupervised learning?
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Supervised learning requires human oversight during training
Unsupervised learning uses unlabeled data, while supervised learning uses labeled data
Supervised learning is used for classification, while unsupervised is for regression
There is no significant difference
I'm not familiar with these terms
21. In the context of neural networks, what does "back-propagation" refer to?
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A method for initializing network weights
The process of feeding data forward through the network
An algorithm for computing gradients and updating weights
A technique for reducing overfitting
I don't know
22. What is the purpose of a loss function in machine learning?
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To increase the model's complexity
To measure the model's performance and guide optimization
To reduce the training time
To add regularization to the model
I'm not sure about loss functions
23. Which of the following is NOT a common activation function in neural networks?
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ReLU (Rectified Linear Unit)
Sigmoid
Tanh
Gaussian
I'm not familiar with activation functions
24. What does the term "overfitting" mean in machine learning?
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When a model performs well on training data but poorly on new, unseen data
When a model is too simple to capture the underlying patterns in the data
When a model takes too long to train
When a model requires too much memory
I don't understand the concept of overfitting
25. Which of the following is a key component of a Convolutional Neural Network (CNN)?
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LSTM cells
Convolutional layers
Decision trees
Support vectors
I'm not familiar with CNNs
26. The rules set by the programmer
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The rules set by the programmer
The strategy the agent uses to determine actions
The reward function
The learning rate of the algorithm
I'm not sure about reinforcement learning terminology
27. What is the primary purpose of cross-validation in machine learning?
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To speed up model training
To assess model performance and generalization
To increase model complexity
To reduce the need for test data
I don't know what cross-validation is
28. Which of the following is NOT a common challenge in Natural Language Processing (NLP)?
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Ambiguity in language
Handling different languages
Image recognition
Contextual understanding
I'm not familiar with NLP challenges
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Stage 7: Additional Open-ended Questions
29. Briefly describe a recent AI development or breakthrough that you find interesting and explain why:
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30. If you were to develop an AI application, what problem would you aim to solve and why?
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