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IIT M QUIZ 1 FOUNDATION DAD DIPLOMA QPD2 16 Oct 2022 - 16 Oct 2022

MLF Quiz 116 questions50 marks11 SA · 3 MSQ · 2 MCQFoundation / Diploma / QPD2

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Q1MCQ0 marks

Q2SA4 marks

Note: Enter the answer correct to two decimal places. question figure

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Q3SA4 marks

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Q4SA4 marks

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Q5MSQ4 marksselect all that apply

Identify which of the following requires use of classification technique.

Credit card fraud detection.
Sentiment analysis.
Predicting whether an email is spam or not.
Predicting whether the patient is having cancer or not.

Q6MSQ4 marksselect all that apply

Q7MSQ4 marksselect all that apply

Q8SA3 marks

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Q9SA3 marks

Note: Enter the answer correct to two decimal places. question figure

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Q10SA3 marks

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Q11SA3 marks

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Q12SA3 marks

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Q13SA3 marks

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Q14SA3 marks

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Q15SA2 marks

Your numeric answer:

Q16MCQ3 marks

Answer key

Q1 (MCQ, 0 marks): A
Q2 (SA, 4 marks): 0.18 to 0.24
Q3 (SA, 4 marks): 0.10 to 0.16
Q4 (SA, 4 marks): 2
Q5 (MSQ, 4 marks): A, B, C, D
Q6 (MSQ, 4 marks): A, D
Q7 (MSQ, 4 marks): A, C, D
Q8 (SA, 3 marks): 2.8
Q9 (SA, 3 marks): 2.10 to 2.40
Q10 (SA, 3 marks): 0.75
Q11 (SA, 3 marks): 3
Q12 (SA, 3 marks): 4
Q13 (SA, 3 marks): 1297
Q14 (SA, 3 marks): -21
Q15 (SA, 2 marks): 1
Q16 (MCQ, 3 marks): A

Concepts this paper tests

Auto-generated summary from the source site. Handy as a checklist; the lessons above explain each idea from scratch.

Summary of Core Topics, Concepts, Principles, and Equations

1. Machine Learning Basics

Classification vs. Regression: Understanding when to use classification techniques (e.g., fraud detection, sentiment analysis) versus regression techniques.
Loss Functions: Calculating loss for classification models, such as the 0-1 loss function.

u(z) = \begin{cases} 1, & \text{if } z \geq 0 \\ 0, & \text{otherwise} \end{cases}

z = 0.5x_1 - x_2 + 0.4x_3

2. Linear Algebra

Vectors and Parallelism: Understanding parallel and perpendicular vectors.
Eigenvectors and Eigenvalues: Identifying eigenvectors of a matrix.
Matrix Operations: Diagonalization, $A = PDP^{-1}$ , and finding the sum of diagonal elements of $A^4$ .
Projection Vectors: Calculating the length of the orthogonal projection vector.
Column Space and Rank: Determining the dimension of the column space of a matrix.

3. Calculus

Limits and Continuity: Evaluating limits and ensuring continuity of functions.

\lim_{x \to 0} \frac{\tan 5x}{\sin 2x} = \frac{5}{2}

Directional Derivatives: Calculating the directional derivative of a function.

D_{\mathbf{u}} f(x, y, z) = \nabla f \cdot \mathbf{u}

\nabla f = (yz, xz, xy)

4. Regression Analysis

Linear Regression: Solving for the weight vector $\theta$ in the equation $X \theta = y$ .
Mean Squared Loss: Calculating the mean squared loss for a regression model.

\text{Mean Squared Loss} = \frac{1}{n} \sum_{i=1}^{n} (y^i - f(x^i))^2

5. Polynomial Fitting

Polynomial Coefficients: Solving for the coefficients $a$ , $b$ , and $c$ in a second-degree polynomial $ax^2 + bx + c$ .

6. Linear Approximation

Linear Approximation: Finding the linear approximation $L$ of a function $f(x, y)$ around a point $(a, b)$ .

L(x, y) = f(a, b) + f_x(a, b)(x - a) + f_y(a, b)(y - b)