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IIT M QUIZ 1 FOUNDATION DAD DIPLOMA QPE3 16 Oct 2022 - 16 Oct 2022

MLF Quiz 116 questions50 marks11 SA · 3 MSQ · 2 MCQFoundation / Diploma / QPE3

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Q1MCQ0 marks

Q2SA4 marks

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

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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 number of emails a user will receive in a single day.
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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Q10SA3 marks

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Q11SA3 marks

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Q12SA3 marks

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Q15SA2 marks

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Q16MCQ3 marks

Answer key

Q1 (MCQ, 0 marks): A
Q2 (SA, 4 marks): 0.38 to 0.42
Q3 (SA, 4 marks): 0.50 to 0.58
Q4 (SA, 4 marks): 3
Q5 (MSQ, 4 marks): A, B, D
Q6 (MSQ, 4 marks): A, D, E
Q7 (MSQ, 4 marks): B
Q8 (SA, 3 marks): 0.6
Q9 (SA, 3 marks): 1.3 to 1.4
Q10 (SA, 3 marks): 1.25
Q11 (SA, 3 marks): 2
Q12 (SA, 3 marks): 8
Q13 (SA, 3 marks): 1297
Q14 (SA, 3 marks): -42
Q15 (SA, 2 marks): 0
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)