Month 1 Assessment Assignment
Modules Covered:
Module 1: Introduction to Artificial Intelligence
Module 2: Foundations of Machine Learning
Purpose
This assignment evaluates what youve learned during the first month of the course. You will
demonstrate your understanding of core AI and machine learning concepts, terminology, ethical
considerations, and how these ideas apply to real-world scenarios.
This is not a memorization exercise, clear explanations, examples, and reasoning matter more than
technical jargon.
Assignment Overview
Total Points: 100
Format: Typed document (Word or PDF)
Length: ~35 pages (not including diagrams, if used)
Part 1: AI Foundations & Core Concepts (25 points)
Answer all questions in complete sentences.
1. What is Artificial Intelligence?
o Define AI in your own words.
o Briefly describe its historical development (early goals vs modern reality).
o Explain one major way AI impacts everyday life today.
2. AI vs Machine Learning vs Deep Learning
o Clearly explain the difference between:
Artificial Intelligence
Machine Learning
Deep Learning
o Provide one real-world example for each.
3. Key Terminology
Choose four of the following terms and explain them clearly:
o Algorithm
o Model
o Dataset
o Features
o Training data
o Prediction
Your explanations should be understandable to someone with no technical background.
Part 2: AI Ethics & Societal Impact (20 points)
Answer the following in short-essay form (12 paragraphs each).
1. Ethical Concerns in AI
Identify and explain two ethical issues related to AI (examples: bias, privacy, surveillance, job
displacement, misinformation).
2. Real-World Impact
Choose one industry (healthcare, finance, education, transportation, hiring, social media, etc.)
and explain:
o How AI is used
o One benefit of AI in that industry
o One ethical or societal risk
3. Your Perspective
Do you believe AI should be more heavily regulated? Why or why not?
Support your answer with at least one concept from the course.
Part 3: Foundations of Machine Learning (30 points)
1. Types of Machine Learning
Explain the difference between:
o Supervised learning
o Unsupervised learning
o Reinforcement learning
For each type:
o Describe how it works
o Give one practical example
2. Data & Features
o What is the difference between data and features?
o Why is feature selection important in machine learning?
3. Concept Check
Explain why more data does not always mean better results.
Part 4: Model Training & Performance (15 points)
1. Training, Validation, and Testing
Explain:
o What training data is used for
o Why validation data exists
o Why test data must be kept separate
2. Overfitting vs Generalization
o Define overfitting
o Define generalization
o Explain why overfitting is a problem in real-world AI systems
Part 5: Applied Scenario (10 points)
Scenario:
A company is building an AI system to predict whether students will pass or fail an online course based
on attendance, assignment completion, and quiz scores.
Answer the following:
1. What type of machine learning would this system most likely use? Why?
2. Name two features that could be used in the model.
3. What is one ethical concern related to using this system?
4. What could happen if the model is overfitted?
Grading Criteria
Criteria Weight
Concept accuracy 35%
Clarity & explanation 25%
Real-world examples 20%
Ethical reasoning 10%
Organization & professionalism 10%
Submission Checklist
Before submitting, make sure:
You answered all sections
Explanations are in your own words
Examples are clear and relevant
Writing is organized and easy to follow
Requirements: 5 parts | Python
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