DADS401 ADVANCED MACHINE LEARNING

Description

SESSION	JULY-AUGUST 2025
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	IV
COURSE CODE & NAME	DADS401 ADVANCED MACHINE LEARNING

 

 

Assignment Set – 1

 

 

Q1. (a) Explain the elements of Time Series Model.

(b) Discuss time series using ARIMA.         5+5     

Ans 1.

(a) Elements of Time Series Model

A time series model is used to analyze data points collected sequentially over time in order to identify patterns and make future predictions. One of the core elements of a time series is the trend component, which represents the long-term movement of data. This trend may show a consistent increase, decrease, or stability over time and reflects underlying structural changes such as economic growth, technological advancement, or population change.

Another important element is the seasonal component, which captures regular and predictable variations occurring at fixed intervals, such as monthly, quarterly, or yearly. Seasonal effects are commonly observed in

 

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Q2. (a) Interpret ARCH Model. Explain its Usage.

(b) Demonstrate some merits and demerits of using AI.    5+5     

Ans 2.

(a) Interpretation and Usage of ARCH Model

The Autoregressive Conditional Heteroskedasticity (ARCH) model is a statistical approach used to analyze time series data characterized by changing variance over time. Traditional time series models assume constant variance, but financial and economic data often display volatility clustering, where periods of high volatility are followed by high volatility and periods of low volatility follow low volatility. ARCH models address this limitation effectivel

 

Q3. (a) Examine any three applications of AI in Medical sciences.

(b) Appraise some challenges or limitations we face with Deep Learning. 5+5

Ans 3.

(a) Applications of AI in Medical Sciences

Artificial Intelligence has significantly transformed medical sciences by enhancing accuracy, efficiency, and patient outcomes. One prominent application is medical imaging and diagnostics, where AI algorithms analyze X-rays, MRIs, and CT scans to detect diseases such as cancer, fractures, and neurological disorders. These systems assist doctors by identifying patterns that may not be visible to the human eye, leading to earlier and more accurate diagnoses.

Another important application is predictive analytics and disease forecasting. AI models analyze patient history, genetic data, and lifestyle factors to predict disease risks and treatment

 

Assignment Set – 2

 

 

Q4. (a) Summarize Back Propagation.

(b) Explain the classification of Layers of CNN.    5+5     

Ans 4.

(a) Summary of Back Propagation

Back Propagation is a fundamental supervised learning algorithm used to train multilayer artificial neural networks by minimizing prediction error. The process begins with forward propagation, where input data is passed through interconnected layers of neurons. Each neuron computes a weighted sum of inputs, applies an activation function, and produces an output. The final output of the network is compared with the expected target value using a loss function such as mean squared error or cross-entropy to quantify prediction error.

Once the error is calculated, back propagation starts its core operation by transmitting this error backward through the

 

Q5. (a) What is Auto-Encoder? Demonstrate its classification.

(b) Describe the concept of LSTM. 5+5     

Ans 5.

 (a) Auto-Encoder and Its Classification

An Auto-Encoder is an unsupervised neural network designed to learn efficient representations of input data through reconstruction. It consists of two symmetrical components: an encoder and a decoder. The encoder compresses the input into a lower-dimensional latent representation, while the decoder reconstructs the original data from this compressed form. The objective is

 

Q6. (a) Discuss the few problems or challenges faced with RL systems.

(b) Reframe some algorithms commonly used with Image recognition system.  5+5     

Ans 6.

(a) Problems and Challenges in Reinforcement Learning Systems

Reinforcement Learning systems face several practical challenges when deployed in real-world environments. One major issue is sample inefficiency, as RL agents often require a very large number of interactions with the environment to learn effective policies. This requirement becomes costly and time-consuming, particularly in physical systems such as robotics, healthcare