Course: Machine Learning
Exam Name: Advanced Machine Learning Techniques
Exam Time: 2 hours
Total Score: 100 points
Instructions:
1. Please answer all questions.
2. For multiple-choice questions, select the best answer.
3. For solution questions, provide a detailed explanation.
4. For calculation questions, show all steps and calculations.
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Question 1 (4 points):
Multiple Choice: Which of the following is a supervised learning algorithm?
a) K-means clustering
b) Decision Trees
c) Apriori algorithm
d) DBSCAN
Answer: b) Decision Trees
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Question 2 (5 points):
Solution: Explain the concept of gradient descent and how it is used to minimize a cost
function in machine learning.
Answer: (No Answer Provided)
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Question 3 (5 points):
Solution: Discuss the difference between bias and variance in the context of machine
learning models. How can these concepts impact the performance of a model?
, Answer: (No Answer Provided)
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Question 4 (6 points):
Calculation: Given a dataset with features X and corresponding labels y, where X is a 100x10
matrix and y is a 100x1 vector. Write the formula for the cost function in a linear regression
model using the mean squared error as the loss function. Calculate the gradient of the cost
function with respect to the weights w (where w is a 10x1 vector).
Answer: (No Answer Provided)
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Question 5 (5 points):
Multiple Choice: In the context of neural networks, what does the term "ReLU" refer to?
a) Repeated Evolution Layer
b) Rectified Linear Unit
c) Relevance Learning Unit
d) Recurrent Layer Update
Answer: b) Rectified Linear Unit
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Question 6 (7 points):
Calculation: Assume you are using a logistic regression model for binary classification. You
have the following log-likelihood function:
L(w) = Σ [y * log(sigmoid(wTX)) + (1-y) * log(1-sigmoid(wTX))]
Where w is the weight vector, X is the feature matrix, and y is the label vector. Derive the
gradient of the log-likelihood function with respect to the weight vector w.
Answer: (No Answer Provided)
---
Exam Name: Advanced Machine Learning Techniques
Exam Time: 2 hours
Total Score: 100 points
Instructions:
1. Please answer all questions.
2. For multiple-choice questions, select the best answer.
3. For solution questions, provide a detailed explanation.
4. For calculation questions, show all steps and calculations.
---
Question 1 (4 points):
Multiple Choice: Which of the following is a supervised learning algorithm?
a) K-means clustering
b) Decision Trees
c) Apriori algorithm
d) DBSCAN
Answer: b) Decision Trees
---
Question 2 (5 points):
Solution: Explain the concept of gradient descent and how it is used to minimize a cost
function in machine learning.
Answer: (No Answer Provided)
---
Question 3 (5 points):
Solution: Discuss the difference between bias and variance in the context of machine
learning models. How can these concepts impact the performance of a model?
, Answer: (No Answer Provided)
---
Question 4 (6 points):
Calculation: Given a dataset with features X and corresponding labels y, where X is a 100x10
matrix and y is a 100x1 vector. Write the formula for the cost function in a linear regression
model using the mean squared error as the loss function. Calculate the gradient of the cost
function with respect to the weights w (where w is a 10x1 vector).
Answer: (No Answer Provided)
---
Question 5 (5 points):
Multiple Choice: In the context of neural networks, what does the term "ReLU" refer to?
a) Repeated Evolution Layer
b) Rectified Linear Unit
c) Relevance Learning Unit
d) Recurrent Layer Update
Answer: b) Rectified Linear Unit
---
Question 6 (7 points):
Calculation: Assume you are using a logistic regression model for binary classification. You
have the following log-likelihood function:
L(w) = Σ [y * log(sigmoid(wTX)) + (1-y) * log(1-sigmoid(wTX))]
Where w is the weight vector, X is the feature matrix, and y is the label vector. Derive the
gradient of the log-likelihood function with respect to the weight vector w.
Answer: (No Answer Provided)
---
