DADS401 ADVANCED MACHINE LEARNING

Description

SESSION	JUL – AUG 2024
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	IV
COURSE CODE & NAME	DADS401 ADVANCED MACHINE LEARNING

 

 

Assignment Set – 1

 

 

1. (a) Explain the elements of Time Series Model?

(b) Discuss ARIMA model for forecasting time series.

Ans 1.

(a) Elements of Time Series Model

A time series model is a mathematical framework designed to analyze and forecast data points collected sequentially over time. The essential elements of a time series model include the following components:

1. Trend Component: The trend represents the long-term direction of the time series, which can be upward, downward, or stagnant. It captures the underlying movement of the data over an extended

 

 

 

 

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2. (a) State the difference between double exponential smoothing (DES) and triple exponential smoothing (TES)?

(b) Explain GARCH Model?

Ans 2.

 (a) Difference Between Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES)

Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) are methods used for time series forecasting. While both methods build upon the concept of exponential smoothing, they address different types of time series patterns.

1. Double Exponential

 

           

3. (a) Write some merits and demerits of using AI.

(b) Explain any three applications of AI in Business Analytics.

Ans 3.

(a) Merits and Demerits of Using AI

Merits of Using AI:

Artificial Intelligence (AI) offers numerous benefits across various fields, making it a transformative technology in today’s world. One of the primary advantages of AI is its ability to automate repetitive and mundane tasks, significantly improving efficiency and reducing human error. For instance, AI-powered systems in manufacturing can streamline processes, enhance quality, and minimize wastage. Additionally, AI excels in data analysis, allowing businesses to derive valuable

 

 

Assignment Set – 2

 

 

4. (a) Explain some challenges or limitations we face with Deep Learning.

(b) Discuss Back Propagation. 5+5 

Ans 4.

(a) Challenges or Limitations of Deep Learning

Deep learning, a subset of machine learning, has revolutionized fields like image recognition, natural language processing, and autonomous systems. However, it faces several challenges and limitations that hinder its widespread application.

1. Data Dependency: Deep learning models require vast amounts of labeled data to train effectively. In many domains, acquiring and annotating such large datasets can be expensive, time-consuming, or

 

 

 

5. (a) Differentiate CNN vs RNN.

(b) Describe the concept of LSTM.

Ans 5.

(a) Differentiation Between CNN and RNN

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two distinct architectures in deep learning, each designed for specific types of data and tasks. Their differences stem from their structural design and applications.

Convolutional Neural Networks (CNNs): CNNs are specialized for processing grid-like data, such as images. They use convolutional layers to extract spatial features by applying filters to local regions of the input

 

6. (a) State the difference between SARSA and Q-Learning.

(b) State the difference between Image recognition and Image detection.

Ans 6.

(a) Difference Between SARSA and Q-Learning

SARSA (State-Action-Reward-State-Action) and Q-Learning are reinforcement learning algorithms used to solve Markov Decision Processes (MDPs). Both methods aim to find an optimal policy for an agent by updating Q-values, but they differ in how they update these values.

SARSA: SARSA is an on-policy algorithm, meaning it updates Q-values based on the policy the agent is currently following. The