Types of ML (supervised, unsupervised, reinforcement) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the type of learning from a description

You have a dataset with labeled images of cats and dogs. You want the model to learn to tell cats from dogs using these labels. What type of machine learning is this?

A Unsupervised learning

B Supervised learning

C Reinforcement learning

D Self-supervised learning

💡 Hint

Think about whether the data has labels or not.

Supervised learning uses labeled data to teach the model. Here, images are labeled as cats or dogs, so it's supervised learning.

❓ Predict Output

intermediate

Output of clustering algorithm example

What is the output of this Python code using KMeans clustering on simple 2D points?

ML Python

from sklearn.cluster import KMeans
import numpy as np
points = np.array([[1,2],[1,4],[1,0],[10,2],[10,4],[10,0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(points)
labels = kmeans.labels_
print(labels.tolist())

A [0, 0, 0, 1, 1, 1]

B [1, 1, 1, 0, 0, 0]

C [0, 1, 0, 1, 0, 1]

D [1, 0, 1, 0, 1, 0]

💡 Hint

Points near (1,*) will be in one cluster, points near (10,*) in another.

KMeans groups points into clusters. Points with x=1 are grouped as cluster 0, points with x=10 as cluster 1.

❓ Model Choice

advanced

Choosing the right ML type for a game AI

You want to build an AI that learns to play a video game by trying moves and getting points as feedback. Which type of machine learning should you use?

A Supervised learning

B Unsupervised learning

C Reinforcement learning

D Semi-supervised learning

💡 Hint

The AI learns by trial and error with rewards.

Reinforcement learning trains agents by rewarding good actions and punishing bad ones, perfect for game playing.

❓ Metrics

advanced

Evaluating clustering quality

Which metric is commonly used to evaluate the quality of an unsupervised clustering model?

A Cross-entropy loss

B Accuracy

C Mean squared error

D Silhouette score

💡 Hint

This metric measures how well clusters are separated.

Silhouette score measures how similar an object is to its own cluster compared to other clusters, useful for clustering evaluation.

🔧 Debug

expert

Why does this reinforcement learning code not update the policy?

Consider this simplified reinforcement learning code snippet. Why does the policy not improve after training?

ML Python

import numpy as np
policy = np.array([0.5, 0.5])
rewards = [1, -1, 1, -1]
actions = [0, 1, 0, 1]
for r, a in zip(rewards, actions):
    policy[a] += 0.1 * r
policy = policy / policy.sum()
print(policy)

A Policy probabilities can become negative causing invalid distribution

B Normalization divides by sum but sum can be zero causing error

C Learning rate 0.1 is too small to update policy noticeably

D Rewards are not used to update the policy correctly

💡 Hint

Check if policy values remain valid probabilities after updates.

Adding negative rewards directly can make probabilities negative, which is invalid for a probability distribution.

Practice

1. Which type of machine learning uses labeled data to teach the model?

easy

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Deep learning

Solution

1. Step 1: Understand labeled data meaning

   Labeled data means each example has a known answer or outcome.

2. Step 2: Match learning type with labeled data

   Supervised learning uses labeled data to train models to predict outcomes.

3. Final Answer:

   Supervised learning -> Option A

4. Quick Check:

   Supervised = labeled data [OK]

Hint: Labeled data means supervised learning [OK]

Common Mistakes:

• Confusing unsupervised with supervised
• Thinking reinforcement uses labeled data
• Mixing deep learning as a type here

2. Which of the following is the correct description of unsupervised learning?

easy

A. Learning from labeled data to predict outcomes

B. Finding patterns in data without labels

C. Learning actions to maximize rewards

D. Training deep neural networks

Solution

1. Step 1: Recall unsupervised learning definition

   Unsupervised learning finds hidden patterns or groups in data without labels.

2. Step 2: Match options with definition

   Finding patterns in data without labels correctly states it finds patterns without labels.

3. Final Answer:

   Finding patterns in data without labels -> Option B

4. Quick Check:

   Unsupervised = no labels [OK]

Hint: No labels means unsupervised learning [OK]

Common Mistakes:

• Choosing supervised for unlabeled data
• Confusing reinforcement with unsupervised
• Thinking deep learning is a type of ML here

3. Consider this Python code snippet for a reinforcement learning agent:

actions = ['left', 'right', 'up', 'down']
rewards = {'left': -1, 'right': 1, 'up': 0, 'down': 0}
current_action = 'right'
reward = rewards[current_action]
print(reward)

What will be the output?

medium

A. 0

B. -1

C. null

D. 1

Solution

1. Step 1: Identify the current action and reward mapping

   The current_action is 'right'. The rewards dictionary maps 'right' to 1.

2. Step 2: Print the reward for 'right'

   reward = rewards['right'] equals 1, so print outputs 1.

3. Final Answer:

   1 -> Option D

4. Quick Check:

   Reward for 'right' = 1 [OK]

Hint: Check dictionary key for current action [OK]

Common Mistakes:

• Confusing keys and values in dictionary
• Printing variable before assignment
• Assuming default reward 0

4. The following code is intended to perform supervised learning but has an error:

data = [([1, 2], 0), ([3, 4], 1), ([5, 6], 0)]
features, labels = zip(*data)
model = SomeModel()
model.train(features, labels)

What is the likely error?

medium

A. Features and labels are not separated correctly

B. Data format is incorrect for supervised learning

C. Model class SomeModel is undefined

D. train method should be fit instead

Solution

1. Step 1: Check variable unpacking

   features and labels are correctly unpacked from data using zip(*data).

2. Step 2: Identify model usage error

   SomeModel is not defined anywhere, so creating model = SomeModel() causes an error.

3. Final Answer:

   Model class SomeModel is undefined -> Option C

4. Quick Check:

   Undefined class causes error [OK]

Hint: Undefined classes cause errors at instantiation [OK]

Common Mistakes:

• Assuming train method is wrong without context
• Thinking data unpacking is incorrect
• Ignoring missing model definition

5. You want to build a system that learns to play a game by trying moves and getting points as feedback. Which type of machine learning should you use?

hard

A. Reinforcement learning

B. Supervised learning

C. Unsupervised learning

D. Clustering

Solution

1. Step 1: Understand the problem context

   The system learns by trying moves and receiving points (rewards) as feedback.

2. Step 2: Match learning type to feedback-based learning

   Reinforcement learning fits because it learns actions to maximize rewards from feedback.

3. Final Answer:

   Reinforcement learning -> Option A

4. Quick Check:

   Learning from rewards = Reinforcement [OK]

Hint: Learning from rewards means reinforcement learning [OK]

Common Mistakes:

• Choosing supervised because of 'learning'
• Confusing clustering with reinforcement
• Ignoring feedback as reward signal