DADS401 ADVANCED MACHINE LEARNING

Description

SESSION	JAN-FEB 2026
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	IV
COURSE CODE & NAME	DADS401 ADVANCED MACHINE LEARNING

 

 

Assignment Set – 1

 

Q.1. (a) Discuss the objectives of Time Series Analysis. (b) Explain Autoregressive model. (5+5 = 10 Marks)

Ans 1.

1. a) Objectives of Time Series Analysis

Time series analysis is a mathematical method that is applied to data that are recorded or collected at successive identically spaced intervals in time. Its primary objective is to learn the structure and fundamentals of temporal data and use that understanding to make accurate predictions of the future’s value. Time series analysis serves several distinct and related goals in applied statistics and data science.

The initial goal is

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Q.2. Interpret ETS Model. Define the ARCH Model. Explain its usage. (5+5 = 10 Marks)

Ans 2.

ETS Model

The ETS model is a reference to Error, Trend and Seasonality. It is a model for exponential smoothing state space models that are used in forecasting time series. The name captures the three elements that the model is composed of and model separately. ETS models form a group of models instead of one model. every component being classified as All, Additive, or Multiplicative, generating the possibility of a wide range of combinations that are able to be fitted to different

Q.3. Describe a few risks associated with Artificial Intelligence. Appraise some challenges or limitations we face with Deep Learning. (5+5 = 10 Marks)

Ans 3.

1. a) Risks Associated with Artificial Intelligence

Artificial Intelligence presents several significant risks that must be carefully controlled as AI technology becomes more pervasive across industries as well as the public sector. Knowing the risks is crucial to ensure the responsible AI development and deployment.

Bias and fairness

 

Assignment Set – 2

 

Q.4. (a) Discuss ANN classification models. (b) Explain the classification of layers of CNN. (5+5 = 10 Marks)

Ans 4.

1. a) ANN Classification Models

Artificial Neural Networks (ANNs) are computer-generated models that draw inspiration from the form and function of neuronal cells in the human brain. They’re composed of interconnected nodes that are organized in layers which handle input data using connections and weighted ones to create output

 

Q.5. (a) Describe the classification of RNN based upon architecture. (b) Illustrate the difference between SARSA and Q-Learning. (5+5 = 10 Marks)

Ans 5.

1. a) Classification of RNN Based on Architecture

Recurrent Neural Networks (RNNs) are the name given to a group of neural networks that are specifically developed to handle sequential data in a hidden state that collects the information of previous times. Unlike feedforward networks that process each input independently, RNNs have the ability to

 

Q.6. (a) Demonstrate the phases we need for doing Neural Network Analysis. (b) Reframe some algorithms commonly used with Image recognition system. (5+5 = 10 Marks)

Ans 6.

1. a) Phases of Neural Network Analysis

Neural network analysis is a planned method that has several steps to construct, verify, and deploy a model that is able to learn from evidence. Each step should be performed with care to ensure that the final model is precise solid, reliable, and adapts easily to data from new sources.

1. The initial phase involves collecting and exploring data, that is the process of obtaining sufficient