B.Sc. (Computer Science) Computer Science Practical 5 Syllabus - Mumbai University
This is the TY BSc Computer Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it — her Summer 2026 third-year timetables name that scheme — so check which scheme your exam form names before you revise.
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Syllabus for Computer Science Practical 5
Module I
- Practical based on Artificial Intelligence
- Breadth First Search & Iterative Depth First Search
- Implement the Breadth First Search algorithm to solve a given problem.
- Implement the Iterative Depth First Search algorithm to solve the same problem.
- Compare the performance and efficiency of both algorithms.
- A* Search and Recursive Best-First Search
- Implement the A* Search algorithm for solving a pathfinding problem.
- Implement the Recursive Best-First Search algorithm for the same problem.
- Compare the performance and effectiveness of both algorithms.
- Decision Tree Learning
- Implement the Decision Tree Learning algorithm to build a decision tree for a given dataset.
- Evaluate the accuracy and effectiveness of the decision tree on test data.
- Visualize and interpret the generated decision tree.
- Feed Forward Backpropagation Neural Network
- Implement the Feed Forward Backpropagation algorithm to train a neural network.
- Use a given dataset to train the neural network for a specific task.
- Evaluate the performance of the trained network on test data.
- Support Vector Machines (SVM)
- Implement the SVM algorithm for binary classification.
- Train an SVM model using a given dataset and optimize its parameters.
- Evaluate the performance of the SVM model on test data and analyze the results.
- Adaboost Ensemble Learning
- Implement the Adaboost algorithm to create an ensemble of weak classifiers.
- Train the ensemble model on a given dataset and evaluate its performance.
- Compare the results with individual weak classifiers.
- Naive Bayes’ Classifier
- Implement the Naive Bayes’ algorithm for classification.
- Train a Naive Bayes’ model using a given dataset and calculate class probabilities.
- Evaluate the accuracy of the model on test data and analyze the results.
- K-Nearest Neighbors (K-NN)
- Implement the K-NN algorithm for classification or regression.
- Apply the K-NN algorithm to a given dataset and predict the class or value for test data.
- Evaluate the accuracy or error of the predictions and analyze the results.
- Association Rule Mining
- Implement the Association Rule Mining algorithm (e.g., Apriori) to find frequent itemsets.
- Generate association rules from the frequent itemsets and calculate their support and confidence.
- Interpret and analyze the discovered association rules.
- Demo of OpenAI/TensorFlow Tools
- Explore and experiment with OpenAI or TensorFlow tools and libraries.
- Perform a demonstration or mini-project showcasing the capabilities of the tools.
- Discuss and present the findings and potential applications.
Module II
- Practical based on Cyber & Information Security
- Implementing Substitution and Transposition Ciphers:
- Design and implement algorithms to encrypt and decrypt messages using classical substitution and transposition techniques.
- RSA Encryption and Decryption:
- Implement the RSA algorithm for public-key encryption and decryption, and explore its properties and security considerations.
- Message Authentication Codes:
- Implement algorithms to generate and verify message authentication codes (MACs) for ensuring data integrity and authenticity.
- Digital Signatures:
- Implement digital signature algorithms such as RSA-based signatures, and verify the integrity and authenticity of digitally signed messages.
- Key Exchange using Diffie-Hellman:
- Implement the Diffie-Hellman key exchange algorithm to securely exchange keys between two entities over an insecure network.
- IP Security (IPsec) Configuration: Configure IPsec on network devices to provide secure communication and protect against unauthorized access and attacks.
- Web Security with SSL/TLS:
- Configure and implement secure web communication using SSL/TLS protocols, including certificate management and secure session establishment.
- Intrusion Detection System:
- Set up and configure an intrusion detection system (IDS) to monitor network traffic and detect potential security breaches or malicious activities.
- Malware Analysis and Detection:
- Analyze and identify malware samples using antivirus tools, analyze their behavior, and develop countermeasures to mitigate their impact.
- Firewall Configuration and Rule-based Filtering:
- Configure and test firewall rules to control network traffic, filter packets based on specified criteria, and protect network resources from unauthorized access.
Text Books
- 1 Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 3rd Edition, Pearson, 2010.
- 2 Cryptography and Network Security: Principles and Practice 7th edition, William Stallings, Pearson
- 1 Artificial Intelligence: Foundations of Computational Agents, David L Poole,Alan K. Mackworth, 2nd Edition, Cambridge University Press ,2017.
- 2 Artificial Intelligence, Kevin Knight and Elaine Rich, 3rd Edition, 2017 3) The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, 2013
- 3 Cryptography and Network Security, 2nd edition, Behrouz A Fourouzan, Debdeep Mukhopadhyay, TMH.
- 4 Atul Kahate, “Cryptography and Network Security”, Tata McGraw-Hill.
Reproduced from the University of Mumbai syllabus for B.Sc. (Computer Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.