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IIT M QUIZ 1 FOUNDATION DAD DIPLOMA QPE2 16 Oct 2022 - 16 Oct 2022

MLF Quiz 116 questions50 marks11 SA · 3 MSQ · 2 MCQFoundation / Diploma / QPE2

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Q1MCQ0 marks

Q2SA4 marks

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

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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 Models: Used to predict categorical outcomes.
Loss Functions: Measure the difference between predicted and actual values.
- 0-1 Loss Function:

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

Mean Squared Loss: Used in regression models.

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

2. Linear Algebra

Vectors and Vector Operations:
- Parallel and Perpendicular Vectors: Vectors are parallel if one is a scalar multiple of the other. They are perpendicular if their dot product is zero.
- Dot Product:

\mathbf{v} \cdot \mathbf{w} = \sum_{i=1}^{n} v_i w_i

Eigenvectors and Eigenvalues: Vectors that change by a scalar factor when a linear transformation is applied.

A \mathbf{v} = \lambda \mathbf{v}

Matrix Operations:
- Matrix Multiplication: $A \theta = y$
- Projection of Vectors:

\text{proj}_{\mathbf{d}} \mathbf{v} = \frac{\mathbf{v} \cdot \mathbf{d}}{\mathbf{d} \cdot \mathbf{d}} \mathbf{d}

Column Space: The set of all linear combinations of the columns of a matrix.

3. Calculus

Limits and Continuity: Understanding the behavior of functions as they approach certain points.

\lim_{x \to a} f(x) = L

Directional Derivatives: The rate of change of a function in a specific direction.

D_{\mathbf{u}} f(\mathbf{a}) = \nabla f(\mathbf{a}) \cdot \mathbf{u}

Gradient:

\nabla f(x, y, z) = \left( \frac{\partial f}{\partial x}, \frac{\partial f}{\partial y}, \frac{\partial f}{\partial z} \right)

Linear Approximation: Approximating a function using its tangent plane at a point.

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

4. Polynomial Fitting

Second Degree Polynomial:

f(x) = ax^2 + bx + c

5. Regression Analysis

Linear Regression: Modeling the relationship between a dependent variable and one or more independent variables.

X \theta = y