The second half of third year deepens the AI&ML core with Natural Language Processing, Deep Learning, and Data Visualization, alongside two further Professional Elective slots and a second Open Elective. Dedicated labs give hands-on practice in deep learning frameworks and visualization tooling, while Soft Skills and a Technical Paper Writing & IPR audit course build communication and research-documentation ability. A mandatory industry internship or mini-project follows over the summer.
Subjects
Natural Language Processing
- Unit 1: Origins and challenges of NLP, including grammar-based and statistical language modeling.
- Unit 2: Word-level analysis — N-gram models, their evaluation, and smoothing techniques.
- Unit 3: Syntactic analysis using context-free grammars and English grammar rules.
- Unit 4: Semantics and pragmatics — representation requirements and first-order logic approaches.
- Unit 5: Discourse analysis and lexical resource construction.
L-T-P: 3-0-0, 3 credits
Deep Learning
- Unit 1: Biological inspiration for neural computation and the McCulloch-Pitts unit.
- Unit 2: Feedforward networks, gradient descent, and backpropagation.
- Unit 3: Modern optimization methods for more effective neural network training.
- Unit 4: Recurrent neural networks, backpropagation through time, and LSTM units.
- Unit 5: Recent developments — variational autoencoders, transformers, and GPT-style applications across vision and language tasks.
L-T-P: 3-0-0, 3 credits
Data Visualization
- Unit 1: What visualization is, its history, and its relationship to related disciplines.
- Unit 2: Creating visual representations — reference models and visual mapping.
- Unit 3: Classifying visualization systems and interaction techniques, including common pitfalls like misleading charts.
- Unit 4: Visualizing groups, trees, graphs, clusters, and networks.
- Unit 5: Visualizing volumetric data, vector fields, and simulation processes.
L-T-P: 3-0-0, 3 credits
Professional Elective-II options:
- Software Testing Methodologies — covers the purpose of testing, transaction-flow testing, path-based testing techniques, state-graph-based transition testing, and graph-matrix approaches to test design.
- Cryptography & Network Security — covers core security principles, symmetric cryptography’s algebraic foundations, block ciphers (DES, AES, Blowfish), cryptographic hash functions, and transport/web-level security.
- DevOps — covers the SDLC-to-DevOps transition, source-code management with Git, build automation and continuous integration, continuous delivery practices, and configuration management with Ansible.
- Recommender Systems — covers recommender-system fundamentals, collaborative filtering, content-based recommendation, hybrid approaches, and methods for evaluating recommender quality.
L-T-P: 3-0-0, 3 credits (one option selected)
Professional Elective-III options:
- Software Project Management — covers conventional (waterfall) project management, lifecycle phases, model-based software architectures, project organization structures, and Agile/Scrum adoption.
- Mobile Adhoc Networks — covers ad hoc and cellular network fundamentals, MANET routing protocols, ad hoc network security, wireless sensor network basics, and WSN security/key management.
- Computer Vision — covers camera models and radiometry, linear filters and convolution, multi-view geometry and stereopsis, model-fitting segmentation (Hough transform), and geometric camera calibration.
- NoSQL Databases — covers the four major NoSQL database types, comparisons with relational databases, key/value and document stores (MongoDB), column-oriented stores (HBase), and Riak-based key/value databases.
L-T-P: 3-0-0, 3 credits (one option selected)
Open Elective-II is drawn from the cross-department elective pool in the same way as Open Elective-I.
Deep Learning Lab
- Building multi-layer perceptrons for MNIST digit classification.
- Designing networks for binary and multi-class text classification (IMDB, Reuters datasets).
- Predicting housing prices with a regression network on the Boston Housing dataset.
- Building convolutional neural networks for digit and image classification, including transfer learning with VGG16.
- Implementing word embeddings and a recurrent neural network for movie-review sentiment classification.
L-T-P: 0-0-3, 1.5 credits
Data Visualization Lab
- Visualizing datasets with histograms and line charts.
- Building bar charts and box plots across multiple datasets.
- Creating scatter plots, mosaic plots, and multi-variable scatter matrices.
- Producing map-based visualizations and heatmaps.
- Building correlograms and 3D visualizations for multivariate data.
L-T-P: 0-0-3, 1.5 credits
Soft Skills
(Skill Enhancement Course)
- Analytical thinking and listening skills, including self-introduction and structured talks.
- Self-management skills — anger, stress, and time management.
- Standard operating methods for communication — grammar, tenses, and pronunciation.
- Job-oriented skills — group discussions and resume preparation.
- Interpersonal relationships — their importance, types, and influencing factors.
L-T-P: 0-1-2, 2 credits
Technical Paper Writing & IPR
(Audit Course)
- Introduction to technical report writing and sentence construction.
- Drafting reports and handling illustrations/graphics.
- Proofreading and summarization practice.
- Using word-processing tools for structured report elements like tables of contents.
- The nature of intellectual property — patents, designs, trademarks, and copyright.
L-T-P: 2-0-0 (non-credit audit course)
A mandatory industry internship or mini-project of 8 weeks’ duration is undertaken during the following summer vacation.
Semester total: 20-1-8 contact hours, 23 credits, with optional Minor and Honors-pool courses available as in the prior semester.