UG Programs | 4 years

B.Tech in Computer Science & Artificial Intelligence

Increasing access to computing power and explosion of digital data coupled with advances in AI are transforming every aspect of human life. The Computer Science & Artificial Intelligence program is not a traditional Computer Science degree, but combines a Computer Science core with artificial intelligence, machine learning and human intelligence with a long term view that computing systems will be pervasive and that the interface of human and artificial intelligence will be a source of future grand challenges and opportunities for innovation.

8 Semester Curriculum The curriculum at Plaksha is dynamic and continuously evolving, based on inputs from faculty, latest research and industry insights. B.Tech in Computer Science & Artificial Intelligence syllabus outline is given below.

Semester 1
Semester 2
Semester 3
Semester 4
Semester 5
Semester 6
Semester 7
Semester 8

Computational Thinking

Instructor(s) - Dr. K Gopinath, Dr. Manoj Kannan

Coding Café (Python & LINUX)

Instructor(s) - Dr.Srinivasan Vishwanathan

Engineering Math in Action

Instructor(s) - Dr. Amrik Sen, Dr. Saikat Chakraborty

Engines of Life

Instructor(s) - Dr. Monika Sharma, Dr. Manoj Kannan

Environmental Science

Instructor(s) - Dr. Prashanth Suresh Kumar

The Art of Thinking and Reasoning

Instructor(s) - Dr. Aditya Malik, Dr. Brainerd Prince

Universal Human Values

Communication Lab

Instructor(s) - Dr. Brainerd Prince

ILGC: Design and Innovation

Instructor(s) - Dr. Amit Sheth, Dr. Rucha Joshi

Innovation Lab & Grand Challenge Studio V

Continuing their project progress from semester 4, the goal for Semester 5 and 6 will be to implement solutions via projects at the State level, with an eye for expansion at the National level. To achieve this, students will seek validation of concept from various stakeholders, complete the engineering design cycle of their project, while also developing an entrepreneurial spirit from their experiences. Mentored Leadership and Professional Development opportunities will be a constant feature across the 4 year ILGC experience, and will be integrated with project work. These serve to develop the student’s professional skills and also help in creating a more integrated socio-integrated understanding of engineering/design.

Computing at Scale

In this course, students will learn how the sequential, uniprocessor system design concepts and components are scaled to solve extremely large-scale computational problems. Specifically, this course seeks to examine the process (including engineering tradeoffs) of how the components - compute, storage, network, and software - scale-up to address the challenges of computing that involves large data subjected to concurrent, fast, reliable, secure, and energy efficient computing. Traditional and evolving models of parallel and distributed computing will be studied against the backdrop of modern applications from fields such as machine learning, high-performance computing, and internet of things.

Databases

The objective of this course is an in-depth study of data storage and its management in filesystems, key-value stores and database management systems, and their respective application domains. After discussing the basics of filesystems, key-value stores and the related NoSQL stores, the course will cover the use of databases as a tool for data management, and the theory and systems concepts behind relational and NoSQL databases. As a part of database design, the course will cover ER diagrams, SQL, schema and view management, query processing and optimization, indexing, data integrity and normalization, transaction management, security, large-scale distributed data processing with NoSQL databases.

Deep Learning & Computer Vision

Large portion of information in our lived environment is available in the form of visual images. Repositories of photographs and videos, ranging widely from true colour to remote sensing imagery, are growing exponentially, and contain a wealth of information. The ability to extract relevant details from visual data is thus a key component of many practical systems (e.g. systems for climate change assessment, disaster response, vehicle navigation etc), and particularly systems that must exhibit artificial intelligence. This course complements the earlier course on machine learning and neural networks whilst going deeper into the realm of machine learning for visual perception. We start from the roots of computer vision, in the fields of signal processing, and more specifically, digital image processing. We develop the notion of convolutional neural networks as a hierarchy of filters adapted to an end task. We discuss different algorithms for visual inference in their original (hand-designed), and current (neural) form. As part of the lab work students will learn to design, implement and benchmark different neural network models for computer vision, and build intuition about the use-cases of these models, and their performance, robustness and safety implications.

Technical Elective I

Students may take courses from other majors as part of the free elective. Additionally, faculty may also offer some introductory electives as part of this sequence.

Application Domain Track II

The Application Domain Tracks are a series of 1 credit modules that help students inculcate skills and mindsets related to research and entrepreneurship. Through these tracks, students will contribute to ongoing research projects in Plaksha's flagship grand challenge research centers, and may work with faculty on their research or on approved external projects in industry/government or startups. Across semesters, students will have the option to work across different disciplinary areas or focus on one area but the purpose is for them to appreciate the relevance of their coursework to a variety of challenges and areas.

Innovation Lab & Grand Challenge Studio VI

Data Mining & Pattern Recognition

This is an introductory course that sits between the domains of statistics, mathematics, machine learning and knowledge discovery. The aim of this course is to give a sweeping exposure to various concepts and mathematical techniques for profiling data sets, analysing them in an open-ended exploratory manner or task-specific targeted manner, building hypotheses and testing them. Along the way, we will cover topics such as data transformation, similarity and dissimilarity indices, data dimensionality reduction, descriptive statistics, predictive modelling, clustering analysis, data visualisation, and evaluation metrics. We will build intuition by working through real world examples. And acquire practical skills by applying our toolkit to reveal patterns and glean insights from wide ranging datasets from the fields of social sciences, medical sciences, natural sciences, financial market, business, etc.

Software Implementation

This course will introduce students to software engineering approaches used in industry, with emphasis on specifying software implementation and testing. Course contents include the Agile software development approach, best practices in coding style, test-case driven development, testing approaches, and software metrics. All through the semester, student teams will participate in a substantial coding project probably catering to the needs of local businesses. In the concluding month, industry speakers will elaborate on the software engineering practices followed in their respective companies.

Natural Language Processing

The course will teach students to build natural language processing systems by processing text, including tokenizing and representing sentences as vectors, RNNs, GRUs, LSTMs and Attention mechanisms for machine translation. Upon completion, the student will be expected to recognize NLP related tasks in day-to-day scenarios and propose approaches that are likely to work well for the scenarios. A brief introduction to Indian NLP will also be part of the course along with a discussion of earlier approaches to NLP.

Technical Elective II

Students may take courses from other majors as part of the free elective. Additionally, faculty may also offer some introductory electives as part of this sequence.

Application Domain Track III

Security of eSystems

In this course, students will learn about different kinds of security problems, with real-life examples, and how to detect and defend against them. The course will broadly cover the following aspects of systems security - basics of security modelling; security policies and mechanisms; hardware security, security of software at programming language level and at the network and web levels; types of attacks and its prevention and defence; basics of cryptography including encryption and decryption algorithms; case studies of serious security incidents and their root cause analyses.

Technical Elective I

Sample Electives include: Cloud Computing, Design of AI Products, Human Computer Interaction, Neural Computing, Data Analytics and Visualisation, Deep Reinforcement Learning, Quantum Computing

Technical Elective II

Sample Electives include: Cloud Computing, Design of AI Products, Human Computer Interaction, Neural Computing, Data Analytics and Visualisation, Deep Reinforcement Learning, Quantum Computing

Human Sciences - Elective I

Sample electives include: Neuroscience, Brain Science, Human Cognition Perception and Memory, Cognitive Psychology, Language and Thought

Innovation Lab & Grand Challenge Studio Capstone

ILGC transforms and culminates as a two semester capstone design project. By the end of the seventh semester a detailed design of the final product (this could be a device, system, process, software, etc. that results from this design experience) needs to be completed. This includes but not limited to the following: Description of the overall project, including a description of the customer and their requirements, the purpose, specifications, and a summary of the approach. Description of the different design approaches considered and evaluation of each design approach. Detailed description of the final proposed design.

Advanced Topics in AI

This course is envisioned to be offered in a seminar format where the instructor will provide motivating elements to spur student-led investigative research in contemporary and emerging topics in the fields of machine learning, artificial intelligence, and data science. Students will learn to engage in scientific investigation and report their findings in seminar format.

Technical Elective III

Sample Electives include: Cloud Computing, Design of AI Products, Human Computer Interaction, Neural Computing, Data Analytics and Visualisation, Deep Reinforcement Learning, Quantum Computing

Human Sciences - Elective II

Sample electives include: Neuroscience, Brain Science, Human Cognition Perception and Memory, Cognitive Psychology, Language and Thought

Humanities & Social Science Elective

Sample electives include: AI for Social Good, Technology, Policy and Law, Decision Making Under Uncertainty, Fairness, Transparency, Accountability, and Ethics in Data Science

Innovation Lab & Grand Challenge Studio Capstone

ILGC transforms and culminates as a two semester capstone design project. By the end of the eighth semester, students will have a working product (this could be a device, system, process, software, etc. that results from this design experience). Therefore, the focus of this semester is to implement, test and evaluate the design approach chosen in your first semester. The following are the expected requirements and deliverables for this semester: Working final product Testing and evaluation of product design Demo of the final product Completed Project Description, Final Reflection and Completed Outcomes Matrix

What can you create?

Recognition Systems

You can build higher quality and safer AI based facial recognition technologies and voice recognition systems to run our processes and applications, biometrics to secure our identities on public systems and a huge number of applications in our daily lives.

Personalized learning systems

You can build AI based personalized learning systems that are paced as per individual preference, adapted to your interests, and have intelligent AI based tutors who help you just where you need it - to make your learning journey effective, adventurous and your own!

AI-based health solutions

You can create AI based healthcare start-ups that help predict and diagnose diseases such as cancer and tuberculosis early with higher accuracy, through deep learning algorithms and image recognition technologies.

Music, art and more

Companies already use big data to help people discover amazing new music and movies aligned to their tastes, find clothes they like better and more. Soon you can even use AI to create hit songs (look up “Not Easy”), create best-selling art and anything else you imagine!

Learning Experiences

Multidisciplinary approach & interdisciplinary perspective

Interacting with and drawing from multiple fields of expertise, making connections between disciplines, analyzing the humanistic, socio-economic, and technical contexts of problems.

Foundational Tech core

Developing an interdisciplinary foundation featuring math, physics, computer science and engineering principles such that they come together as an integrated whole, not segmented topics.

Design & research ability

Identifying and understanding human needs and solving problems creatively through sustained critical investigation.

Innovation & entrepreneurship mindset

Finding opportunities, identifying business models, embracing ambiguity to create value in the marketplace or in society.

Societal responsibility

Considering the social and human consequences of actions and responsibilities to others in local, national, and global communities; acting to improve the human condition.

Leadership & self-reflection

Creating impact through actively developing skills like communication, creativity, critical thinking and collaboration.

Experiential Learning

Integrated learning experience across 4 years of B.Tech in computer science with artificial intelligence. You will work on authentic, real world projects through industry and community engagement or by research with faculty.