Curriculum

This course provides a comprehensive introduction to multivariable calculus and ordinary differential equations through the lens of mathematical modeling and computation. The course begins with the mathematical representation of curves and surfaces using Cartesian, cylindrical, spherical, and parametric forms, followed by the study of smoothness, continuity, differentiability, gradients, directional derivatives, and geometric properties of surfaces. It then develops techniques for measuring geometric and physical quantities, including curve length, surface area, volume, mass, and averages using single, double, and triple integrals.

The course concludes with analytical and numerical methods for solving ordinary differential equations and systems of differential equations arising from real-world applications. Throughout the course, Python-based computational tools are integrated to visualize mathematical objects, perform symbolic and numerical computations, and explore applications in science and engineering.

People move through life accepting what authority figures, books, or media present as true. Educational systems rarely teach rigorous questioning of the knowledge on offer, and unexamined assumptions quietly shape what one perceives, thinks, and does. Five hundred years ago, Europeans believed the earth was flat; for common people and scientists alike, this was not a belief to be questioned but an accepted fact. The history of knowledge is full of such constraints.

This course develops the skill of critical and scientific thinking: examining presuppositions, reasoning from evidence, and building habits of rigorous inquiry. Students learn to identify implicit beliefs, investigate assumptions, and respond to complex problems with greater clarity and intellectual honesty.

Coding Café is a hands-on introductory Python programming course. Students write programs from the ground up, focusing on logic, control flow, data structures, and algorithmic thinking with minimal theoretical overhead. The course prioritizes clarity in problem formulation, code readability, and iterative problem-solving over rote syntax, preparing students for advanced computing courses. Experimentation and ‘fail fast’ iteration are encouraged over memorization; clarity of thinking matters more than syntax perfection.

This course emphasizes experimentation, play, and iteration over memorization. Students are encouraged to explore and fail fast during labs; clarity of thinking matters more than syntax perfection.

Fundamentals of Computational Thinking is an introductory course for first-semester undergraduate students at Plaksha. It introduces programming and builds foundations in computational thinking and problem solving for advanced work in algorithms and programming, as well as for students pursuing other majors.

Students learn fundamental programming approaches, including conditional branching, iteration, recursion, and exhaustive search, and use them to solve problems. The course uses C and develops programming logic through illustrative problems such as checking whether a string is a palindrome.

This course provides an introduction to computational methods for solving optimization problems. Students develop the vector calculus foundations needed for optimization, explore applications in operations research and engineering, and learn linear programming and constrained optimization techniques. Laboratory work gives students practice applying these methods through projects.

This course introduces students to data science and artificial intelligence. Students work with different types of data, form hypotheses and regression models, learn the foundations of intelligent systems, and study techniques for building them. The course also surveys AI areas such as natural language processing and robotics.

Taking a design-centered approach to solving problems and developing technology-driven ideas in entrepreneurial settings, this course introduces core design thinking principles and their application to the creation and implementation of solutions. Tools are provided to navigate each stage of the design thinking process. Through project-based learning and real-world case studies, students gain practical insight into how the methodology fosters innovation and user-centric thinking.

This course introduces the fundamental principles of statics, electrical circuits, and mechatronics that form the foundation of modern engineering systems. Students will develop analytical and practical skills for modelling, analyzing, and designing systems that integrate mechanical and electrical components.

• Statics topics:  Adding vectors, components, unit vectors, dot product, cross product with drawings, components and computers; Free-Body Diagrams, force and moment balance for a particle, a rigid object, trusses, mechanisms and frames.
• Circuits topics: Voltage, current, Kirchhoff’s laws, analysis of resistor circuits, Wheatstone bridge, Thevenin and Norton Equivalent circuits, diodes, op amps, instrumentation amps,  simple sensors.
• Mechatronic topics:  Magnets and electromagnets, motors, commutation, strain gauges, force and distance measurement, transmissions, energy conservation and energy loss.

In the Fundamentals of Economics course, we develop models of how households make consumption decisions and then aggregate those results to the market level. We then turn to the supply side of markets, engaging in a detailed investigation of how firms make production decisions. Next, we combine demand and supply to understand how prices of goods are determined in perfectly and imperfectly competitive markets. The course will take a closer look at economic notions of efficiency, and the ever-present tradeoff between efficiency and equity. We will also take time to consider fundamentals of utility, game theory, and market failures – such as externalities (e.g. air pollution). This is important for understanding how demand and supply is derived for various goods and services and how changes in our environment (man-made or natural), can impact the magnitude and distribution of well-being.

In addition to understanding analytical models in Microeconomics, we will turn to Fundamentals of Macroeconomics. This is the study of the economy, focusing on large-scale economic variables such as national income, overall output, unemployment, inflation, and economic growth. It examines how aggregate demand and supply interact to determine the performance of an economy, and how fiscal and monetary policies influence these dynamics. Studying macroeconomics is vital for making sense of real-world issues like financial crises, inflationary pressures, and global trade dynamics. Topics in macroeconomics that will be covered include measuring the national income and cost of living, unemployment, the open-economy model, and the trade-offs between inflation and unemployment.

The broad goal of The Innovation Lab and Grand Challenges (ILGC) course is to enable students to observe, relate to, and solve societal challenges in a sustainable manner using the technical and engineering skills they will learn concurrently in other courses. These challenges may be drawn from their surroundings, including

• the campus where they will live for the next four years,
• the place they belong to: their city, town, village, state, or country, or
• global challenges identified through competitions and other opportunities they may aspire to pursue.

The course explores how simple observations can lead to ideas, solutions and ultimately products that address real-world problems. Through these success stories, students engage in discussions on empathy, observation, problem-solving techniques, lateral thinking and teamwork. The course also acts as a catalyst, encouraging students to identify and address similar opportunities and challenges in their own surroundings.

Building on the empathy, observation, lateral thinking, and teamwork developed in ILGC-I, this course focuses on breaking down complex societal challenges into specific, actionable problems, and identifying context-appropriate solutions. Students begin by examining campus-based gaps and proposing executable solutions, using tools such as gap analysis, root cause analysis, semi-structured interviews, and focused group discussions to gather primary data on underlying issues. A four-week summer community attachment follows, where students engage in hands-on work to identify real-world grand challenges across diverse contexts. Through the semester, students work both individually and in teams, culminating in an exhibition that presents the observed challenge, its root causes, and proposed solution(s).

Building on the foundational knowledge and exploratory skills from previous semesters, this course engages students with the complexities of grand challenges. Students brainstorm solutions to identified problems and evaluate their social and financial viability. The course emphasizes problem framing, perspective taking, negotiation, social and financial viability checks, and the application of learned concepts through active class discussion.

Designed for first-year undergraduate engineering students, this course develops foundational communication competencies through structured practice in reflection, presentations, conversations, and teamwork. It introduces communication as an essential engineering capability, enabling students to think critically, communicate clearly, collaborate effectively, and reflect on their own growth from the outset of their university journey.

Engineering graduates consistently face a gap between technical ability and communication skills, one that limits their capacity to collaborate and lead. This course addresses that gap by building cognitive, linguistic, and foundational communication skills, such as speaking, listening, reading, and writing, that are rarely part of a standard engineering curriculum.

Reading skills developed in the course equip students to locate relevant information, engage with current developments, and acquire new knowledge in a rapidly evolving field. Writing skills enable students to identify debates, articulate viewpoints, and produce clear, coherent academic papers; competencies that transfer directly to professional contexts. The course follows a transdisciplinary approach; writing assignments are completed in collaboration with other courses.

This course introduces robotics and cyber-physical systems through hands-on lab activities, assignments, projects, and guest lectures spanning research and practice. Topics include sensors and actuators, system modelling, kinematics, dynamics and controls, perception, planning and navigation, cyber-physical systems, communication, and hardware. Students graduate from the course able to design, build, and evaluate simple robotic and cyber-physical systems.

This comprehensive course on Probability and Statistics integrates theory with hands-on Python programming. Topics cover descriptive methods, probability concepts, random variables, statistical distributions, joint densities, sampling methods, and the Central Limit Theorem. Advanced modules address hypothesis testing, p-values, and statistical inference. Lab sessions build practical skills in data visualization, simulation, and implementation of probability rules. Students leave equipped to model uncertainty and draw informed conclusions from data in diverse contexts.

Students learn to build devices that use sensors and actuators controlled by a microprocessor. The course uses the Arduino "ecosystem" with a Raspberry Pi Pico computer, paired with sensors, lights, and motors, enabling students to build machines that can sense, think, and act.

This course offers an exploration of the remarkable machinery shaped by nature, spanning from human organ systems down to the intricate world of cells and genetic material. The course consists of four modules: Human Physiology, Fundamental Biology, Immunology, and The Science and Art of Biomimicry. In the Human Physiology module, learners will gain insight into the functionality of different organ systems and their regulation, essential for maintaining optimal bodily function. Moving on to the Fundamental Biology module, we take a deep dive into the mesmerizing micro and nano machinery present within cells and biomolecules. In the module on Immunology, we explore the intricate defence mechanisms that safeguard the human body. Finally, in the module on Biomimicry, we engage in captivating discussions about real-world design and engineering solutions, all inspired by natural designs. By the end of the course, the students will have a comprehensive understanding on the different mechanisms by which natural systems operate and will be able to connect these concepts and relate them to real world applications.

Building on Computational Thinking, this course develops students' ability to apply Object-Oriented Programming (OOP) principles through problem-solving using common data structures and algorithms. Topics covered include:

• Principles and application of OOP.
• Program complexity, recursion, and proofs by induction.
• Common data structures and algorithms, and their applications.
• Comparative analysis of data structures and their optimality.

What is technology? An instrument, a phone, a rocket, an electric car? An idea? A particular way of knowing the world? And what is its relationship to society: does technology shape society, or does society shape technology, or does that framing itself need to be rethought?

This course rigorously enquires these questions through the perspectives of philosophy, history, social anthropology, human evolution, and civilizational studies. Drawing on examples from the past and present, it asks what the relationship between technology and society could look like in the future. Central to the course is understanding technology through a threefold matrix of thinking, knowing and making.

 Artificial Intelligence (AI) is transforming every aspect of human life and raising urgent questions about productivity, creativity, employment and sustainability. This course introduces AI to engineering students with no prior experience in the field. It traces the historical evolution of AI, its applications across daily life and industry, and the ethical and environmental implications of its use. Students leave with a grounded understanding of AI terminology, its diverse applications, and the societal responsibilities that come with building and deploying these technologies.

Campus life brings new experiences alongside uncertainty and challenge. Universal Human Values II is designed to help students make sense of both. Over 15 interactive and reflective sessions, students examine the people, habits, and attitudes that have shaped them, develop skills in emotion regulation, and build healthy ways to cope and move forward.

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 This course offers a conceptual and practical introduction to deep learning. Module 1 covers the building blocks: different types of neural networks (convolutional, recurrent, graph) and effective embeddings through state-of-the-art architectures including attention modules, transformers, memory networks, and GPT. The module also addresses perception and generation in text and images. Module 2 reinforces these foundations through applications in NLP (summarization, sentiment analysis, and translation) and in computer vision (object detection, segmentation, monocular depth estimation, stable diffusion, and GANs). The course concludes with advanced topics: self-supervised learning, energy-based models, and stable diffusion. A major project component requires students to engage extensively with research papers and code.

This course covers the design and analysis of fundamental algorithms used in practice across three areas. The first is complexity measurement: analyzing the time and space complexity of algorithms under worst-case and average-case scenarios using asymptotic notations including big-oh, big-omega, and theta. The second is algorithm design paradigms, covering divide-and-conquer, dynamic programming, and greedy approaches. The third is the design of efficient polynomial-time algorithms for fundamental problems in computer science. The course also introduces complexity theory, examining a class of decisional problems, referred to as hard problems, for which deterministic polynomial-time algorithms are believed to be intractable.

This course introduces students to the basics of discrete mathematics, also known as finite or concrete mathematics. The students will study various concepts in logic, set theory, functions and relations, graphs, and modular arithmetic, among others. In addition to the theoretical concepts, the theory classes will cover some common mathematical notation and language, proof techniques, and problem-solving strategies. The tutorials will cover several examples in greater depth.

This course builds deep knowledge of computer systems, spanning processor architectures, peripheral devices, and communication interfaces. Students explore multicore, superscalar, SIMD, MIMD, and FPGA-based architectures through contemporary case studies including ARM Cortex, Intel Core, and Apple M1/M2 processors. Essential peripherals (HDD, SSD, NVMe, GPU) and modern interfaces including PCI Express, USB standards, Thunderbolt, and M.2 are examined in detail. The course places particular emphasis on hardware-software co-design, covering instruction set architectures (RISC-V, ARM), pipelining, memory hierarchies, caches, and virtual memory management. Students also develop system-level programming skills in assembly language, interrupt handling, device driver basics, and system debugging tools including GDB, Valgrind, perf, and strace.

Game theory is a branch of mathematics and economics that provides a systematic framework for studying outcomes that depend on the choices and decisions of multiple players. Its objective is to predict outcomes and design mechanisms that can steer players toward desired outcomes.

This introductory course covers the theory and application of non-cooperative games. It discusses two important formulations of games: static games, which are one-shot games, and sequential-move games, along with the solution concepts used to analyze their outcomes. One-shot games can be used to analyze scenarios such as the Prisoner's Dilemma, markets in which firms compete with one another, and electoral competition. Sequential-move games are more representative of real-world situations, which are typically sequential in nature. Examples include war-like scenarios, markets in which one firm moves before others, and decision-making situations in which an individual must repeatedly choose whether to proceed with a particular course of action.

 The course applies game-theoretic concepts to such real-world scenarios and also introduces the theory of mechanism design, which builds on the foundations of game theory. An example of a mechanism is an auction, which is widely used in practice to allocate goods and services efficiently. The course explores mechanism design through relevant and practical real-world-inspired examples.

This course covers the design, analysis, and methodology of algorithms used to recognize patterns in real-world data: images, audio, video, text, speech, financial data, biosensing, and medical data. It is the foundational course in Artificial Intelligence, which has reshaped how the world operates, from online search (ChatGPT) and voice recognition ("Hey Google!") to facial recognition (iPhone screen lock) and medical diagnosis (DeepMind).

Machine Learning has become one of the most interdisciplinary fields in engineering, with applications spanning physics to psychology, medicine to meteorology, and politics to philosophy. Since the field encompasses hundreds of algorithms and mathematical concepts, this course does not attempt an overview of each. Instead, it builds a strong fundamental grounding in core topics including feature clustering, dimensionality reduction, classification, and neural networks, equipping students to undertake real-world projects and build end-to-end applications.

Operating systems are fundamental to computer science and software engineering. A central question is how applications interact with hardware resources efficiently and safely, and which mechanisms enable multiple applications to run simultaneously while sharing resources. This course addresses these questions.

The theory of operating systems comprises three main areas: process management, memory management, and I/O management. The course begins with process management, introducing a simple model of computation called a process, followed by an enhanced model known as threads. In memory management, students learn about virtual memory and its implementation through paging. The final component, I/O management, covers device drivers and file systems.

Throughout the course, theoretical concepts are reinforced through practical examples drawn from the xv6 operating system.

This course introduces the fundamental concepts, algorithms, and real-world applications of reinforcement learning (RL). Topics include Multi-Armed Bandits (value functions, UCB, Thompson Sampling), Markov Decision Processes (Bellman equations, policy and value iteration), Monte Carlo Methods (on-policy and off-policy learning), and Temporal Difference Learning (TD(0), SARSA, Q-Learning). The course also covers Policy Gradient Methods (REINFORCE, Policy Gradient Theorem), Deep Reinforcement Learning (DQN, Actor-Critic), and advanced topics including Multiagent RL and Inverse RL. Through coding assignments, projects, and real-world case studies, students develop a strong theoretical foundation and hands-on experience applying RL techniques to AI, robotics, and autonomous systems.

For an autonomous agent to behave intelligently, it must be able to solve problems: arriving at decisions that transform a given situation into a desired goal state, and anticipating the consequences of those decisions to identify ones that work. This course covers a wide variety of search methods that agents can employ for problem-solving.

A fundamental question in computer science is whether all computational problems are solvable by existing computers, and which among them can be solved efficiently. This course addresses both. The theory of computation spans two main areas: automata theory and computability theory. The course begins with automata theory, introducing finite automata as a simple model of computation, followed by enhanced models including pushdown automata and context-free grammars. In computability theory, the course introduces the Turing machine as an advanced model of computation and demonstrates that certain problems lie beyond even its capabilities. Automata theory finds applications in compiler construction, program analysis, and natural language processing.

Advanced Natural Language Processing (NLP) is designed for students who aspire to pursue cutting-edge research in NLP. The course introduces core NLP tasks, including syntactic, semantic, discourse, and pragmatic analysis, along with the computational methods used to address these problems. Emphasis is placed on modern neural network–based approaches, covering fundamental modeling techniques and learning algorithms that underpin contemporary NLP systems. It also focuses on the theoretical foundations and practical methods required to build state-of-the-art language technologies.

Natural language data is central to modern AI systems and is being generated at an unprecedented scale across text, speech, and multimodal platforms. Making sense of this rich and complex data requires principled modeling, linguistic insight, and advanced learning techniques. Students study core NLP tasks such as syntactic, semantic, discourse, and pragmatic analysis, and explore contemporary neural approaches including representation learning, sequence modeling, attention mechanisms, transformers, and large language models. The course also covers probabilistic reasoning, learning from limited and noisy data, and evaluation methodologies, while situating NLP within the broader AI ecosystem.

Through hands-on assignments and a research-driven semester project, students analyze real-world language data, reproduce and extend recent research papers on an NLP topic of their choice, and gain experience with cutting-edge areas such as multilingual and low-resource NLP, generative AI, and ethical considerations in language technologies.

Large language models (LLMs) like ChatGPT and Gemini have moved well beyond technical circles. They are reshaping how software is built and used across industries, while dramatically lowering the barrier to creating functional prototypes. Demand for software solutions spans every context, from small businesses to multinational corporations.

This course covers just enough web development and LLM fundamentals to enable students to build these prototypes. Key skills include web interface development, database development, API creation, and building LLM-based user experiences. While the course focuses on web applications, the LLM concepts covered apply across a wide variety of software contexts.

Computational Social Science (CSS) is an interdisciplinary field that uses computational methods to study social phenomena, social structures, and collective behaviour, drawing on tools from computer science, data science, and the social sciences. This course introduces computational techniques for studying societal issues, with a particular focus on large-scale data analytics, social network analysis, and natural language processing.

The course concentrates on issues prevalent in online social media platforms: human behaviour and interactions in online movements (social and political), hate speech detection and mitigation, the impact of misinformation, mental health awareness, and the formation of echo chambers and polarization. Each topic is examined through three lenses: its contemporary relevance, the computational requirements it presents, and the state-of-the-art techniques used to address it. The course also includes a dedicated session on the ethical collection and analysis of data. Tools covered include Gephi and Python libraries for data pre-processing, analysis, and visualization.

Cryptography provides the fundamental building blocks for secure communication and information security. This course introduces the principles and practices of modern cryptography, with an emphasis on two key areas:

• The design and analysis of core cryptographic primitives such as encryption schemes and digital signatures, and
• Their application in securing real-world systems to ensure data confidentiality, integrity, authenticity, and non-repudiation. Topics include classical ciphers, private- and public-key cryptography, block ciphers, hash functions, message authentication codes (MACs), digital signature schemes (e.g., RSA), and security protocols such as TLS and HTTPS.

Sensors generate vast amounts of data. In autonomous robots, drones, and spacecraft, data is used to predict the position, velocity, and orientation of a vehicle: a procedure known as observer design. As sensor technology has grown, so have the sources: cameras, lidars, sonars, doppler, and lasers.

The course begins with a concrete problem: predicting the position and velocity of a translating object such as a car, using a sensor such as a lidar or tachometer. Simple approaches like signal differentiation and integration are examined, along with their limitations. This leads to a mathematically non-rigorous introduction to the concept of an observer/filter, which is then designed to perform the same task, with its advantages and disadvantages assessed. The course then generalizes this exercise to problems of larger dimension, working in n-dimensional Euclidean state-space.

This progression leads to the Kalman Filter, one of the most powerful algorithms in signal processing, data prediction, and control. Proposed by R. E. Kalman in 1960, it extracts true information from noisy sources. Its applications span cellphones, drones, aircraft, GPS, orbiting spacecraft and satellites, and the 1969 moon landing.

This advanced undergraduate course covers the engineering principles of embedded systems with an emphasis on Cyber-Physical Systems (CPS), addressing the joint dynamics of computer hardware, software, networks, and physical processes. Theory and lab components receive equal weightage. The theory component takes a balanced approach to modelling, design, and analysis of embedded systems. Lab sessions provide hands-on training in hardware-software co-design, culminating in building and testing a functional prototype. This includes working with microcontroller units such as ESP32 and STM32 to interface with sensors and actuators, and incorporates concepts of power management, packaging, concurrency, Real Time Operating Systems (RTOS), and Tiny ML.

This is an introductory course on Graph Neural Networks. Many systems in nature and in human-made settings are complex, with each part interacting with the whole. Networks provide a fundamental way to model such systems. By studying local properties of a network, one can infer information about the entire system, reflecting local-to-global phenomena that appear across modern mathematics. Students will develop the ability to work with network and graph data and use mathematical abstractions to formulate questions about datasets whose structure is not immediately apparent.

Module 1 motivates graphs and highlights their prevalence in both natural and human-made physical systems. It also introduces classical graph invariants and problems. Module 2 focuses on the spectrum of adjacency and Laplacian matrices and their relationship to information in networks. Module 3 covers node embeddings and the neural message passing framework, which is central to Graph Neural Networks. It also discusses attention mechanisms and the role of spectral methods in this context.

Computer Interaction but extending well beyond traditional computers. Technology is now embedded across wearables, smartphones, advanced driver assistance systems, and social media, and the course reflects this breadth through a multi-modal approach.

Students learn to use bio-sensors, computer vision, and electro-mechanical sensors to detect and model human physiological and behavioral responses including neural activity, facial expressions, heart rate variability, pupillometry, and galvanic skin response. Core topics include human factors, ergonomics, cognition, affective computing, and human-centered AI. Practical applications span wearables, autonomous vehicles, and robotics, with emphasis on design principles, engineering solutions, usability, safety, and user experience. The course also addresses ethical and technical challenges in developing human-centric technologies.

Industry relevance is central: the course studies strategies to enhance safety, productivity, and creativity across the full spectrum of working environments, from blue-collar to information workers.

This course equips students with the knowledge and practical skills required for modern IT environments, covering next-generation IT infrastructures, intelligent systems, and emerging technologies. Topics include routing and switching, software-defined solutions, wireless technologies, network security and management, cloud computing, and virtualization. The course includes detailed coverage of platforms including AWS and Microsoft Azure, with a focus on building scalable IT solutions. Students gain a balance of theoretical grounding and hands-on experience.

Thinking requires creating and manipulating mental models that encapsulate knowledge of the world. In classical AI, these representations are symbolic and explicit, contrasting with neural networks where knowledge is buried in connection weights. Symbolic representations are interpretable, which is desirable in human-machine interaction. Just as formal mathematics addresses complex problems with precision, logic-based representations enable succinct and unambiguous knowledge representation.

The course covers propositional and first-order logic, tractable subsets including Horn clause and description logics, event calculus for time and change, and epistemic logic for agent knowledge in multi-agent systems. It also addresses inheritance with taxonomies and default reasoning under incomplete information.

LLMs place heavy demands on the systems that serve them: a single forward pass moves gigabytes of weights and cache data through the memory hierarchy. The deployment costs depend heavily on the throughput and latency one can extract from the GPUs. This course studies how modern LLMs are made to run efficiently, working from the hardware upward. We begin with an analytical framework for reasoning about performance: arithmetic intensity, the roofline model, and careful accounting of FLOPs and bytes a transformer layer consumes. We then treat the GPU as a machine, discussing its memory hierarchy and execution model. We also study the CUDA programming model used to write high-performance kernels, with matrix multiplication as the running example. The second half of the course focuses on computational characteristics of LLM inference (prefill, decode, KV-cache) and the system techniques that make serving practical at scale.

This introductory course is intended for final year students. The Game Theory course offered in Year 3 is a prerequisite. Building on the mathematical modelling of interactions covered in that course, students now focus on designing mechanisms (the rules of the game) to ensure desired outcomes are achieved. Specifically, when rational and intelligent players participate, their utility is maximized when their actions align with the game designer's objectives.

This course introduces students to the basics of computational complexity theory. They will study various concepts in models of computation, time and space complexity, hierarchy of complexity classes, diagonalization techniques, NP-completeness, and oracle/relativized computation. In addition to the theoretical concepts, the theory classes will cover some standard theoretical notions and definitions, proof techniques, and techniques for separating various complexity classes. The tutorials will take several exercises and examples and cover the topics in greater depth.

This course focuses specifically on the nuances of venture investing at both early stage and late stages. Students will learn to navigate diverse venture investment scenarios, including seed funding, Series A investments, and pre-IPO rounds. Through practical exercises, case studies, and interactive workshops, participants will develop comprehensive skills in deal evaluation, due diligence, financial analysis, and strategic decision-making tailored for venture capital. This course serves as a continuation of the Entrepreneurial Finance course offered in the fourth semester, building upon foundational financial concepts.

This course begins by examining the civilizational idea of India—its cultural continuity, 
philosophical depth, and longstanding traditions of knowledge-making. Students engage with 
foundational concepts from the Darshanas, the Upanishads, and the epics to understand how 
India historically conceived of ethics, governance, human flourishing, and social harmony. A 
central focus of the course is the Arthashastra, which provides one of the world’s earliest and 
most sophisticated frameworks for statecraft, diplomacy, economic management, and public 
administration. Students study Kautilya’s sophisticated models of statecraft, intelligence, 
economic planning, and innovation, and connect them with contemporary domains such as AI 
governance, cybersecurity, digital infrastructure, data sovereignty, and technological autonomy. 
By placing ancient strategic thinking in dialogue with modern technological challenges, the 
course equips students to envision India’s role in an increasingly competitive global tech 
landscape.
Drawing from both classical thought and contemporary policy frameworks, students engage 
with forward-looking questions around India’s technological future: How can India build human-centric AI grounded in its philosophical traditions? What might a dharma-based framework for 
data governance look like? How can ancient models of education and knowledge transmission 
inspire 21st-century digital learning ecosystems? What forms of strategic autonomy in 
technology are essential for India to emerge as a global leader?

 This course helps students harness emerging technologies to drive business innovation across products and services, processes, and business models. Whether launching a startup or optimizing corporate strategy, students will explore how cutting-edge technologies fuel both sustaining and disruptive innovation, because not all change is revolutionary, but all progress matters. Through hands-on projects, case studies, and spirited discussions, students will learn frameworks to build systems for innovation, measure impact, and create scalable, market-ready solutions.

An interdisciplinary, case-based overview of the strategic and analytical skills essential for consultants in top-tier strategy firms, this course is built around three core pillars: structured thinking and problem-solving, quantitative analysis, and persuasive communication, with emphasis on a hands-on approach to learning. Through case studies from companies such as Netflix, Disney, and Uber, students develop the ability to dissect complex business problems, craft data-driven recommendations, and communicate insights that influence key stakeholders. Students gain a strong foundation in strategic analysis and practical consulting techniques applicable across industries.

Taking a design-centered approach to solving problems and developing technology-driven ideas in entrepreneurial settings, this course introduces core design thinking principles and their application to the creation and implementation of solutions. Tools are provided to navigate each stage of the design thinking process. Through project-based learning and real-world case studies, students gain practical insight into how the methodology fosters innovation and user-centric thinking.

What philosophical debates took place in early modern India, and what did it mean to lead a philosophical life at this time? This course explores philosophical arguments, changing concerns, and shared questions among philosophers active in India from the sixteenth to eighteenth centuries, reading them in their own words (in English translation). With major intellectual centers ranging from Banaras to Thanjavur, this period stands as an exceptionally rich time of philosophical writing.

Arguments are examined in areas including metaphysics, ontology, emotions, linguistics, reality, and existence, drawing on philosophers associated with Vedanta, Mimamsa (Vedic Hermeneutics), Jainism, Logic (Nyaya and Mantiq), and Linguistics, situated within their historical context.

This flagship course is where ideas meet execution. The Entrepreneurial Challenge Lab immerses students in the real-world experience of building a startup from idea to impact. Working in teams, students engage in customer discovery, prototype testing, market validation, and business model development. With a strong emphasis on learning by doing, the course goes beyond theory to develop the mindset and behaviours of successful entrepreneurs. Starting with an idea or opportunity space, students spend the semester refining it through feedback, mentorship, and real-world engagement, leaving not only with a venture concept but with practical tools to turn problems into scalable solutions.

The course equips aspiring startup founders with the financial acumen needed to build, scale, and sustain high-growth ventures. Through six focused modules, students will develop a strong foundation in financial statement analysis, cap table management, venture capital fundraising, valuation techniques, financial ratio interpretation, and unit economics.

Blending theory with hands-on application, the course includes practical exercises such as dissecting income statements, constructing cap tables, and modeling CAC vs. LTV. Students will learn how to effectively apply key financial concepts to strategic decision-making and communicate with investors in entrepreneurial contexts.

Leadership is essential for anyone navigating technology-driven careers, whether launching a startup, managing a team, or driving innovation. This intensive bootcamp, led by instructors from UC Berkeley's Sutardja Center, prepares students to lead in high-stakes, fast-paced environments. Through interactive exercises and real-time simulations, students explore leadership styles, group dynamics, crisis response, and decision-making under pressure, reflecting on their leadership identity and building practical tools for self-awareness, influence, and leading through disruption.

This interdisciplinary course examines how Artificial Intelligence (AI) is reshaping the way we work, innovate, and build careers. Through guest sessions with entrepreneurs, thought leaders, and industry experts, students will explore the opportunities and challenges of AI across areas such as the future of work, ethical AI, the creator economy, and sustainable entrepreneurship. Designed to spark curiosity and entrepreneurial thinking, the course encourages students to reimagine businesses and careers that are resilient, ethical, and impact driven. Whether launching a startup or joining an AI-powered enterprise, students will gain strategic insights to navigate and thrive in the AI era.

We call the earth our home, but do we ever really turn to ‘face’ it? This course explores questions around what the earth is, our relation to the earth, and how earth has been discussed in philosophy, history, and science. The goal of the course is an interdisciplinary investigation into the earth and closely associated concepts such as planet, world, and globe. Exploring earth in an interdisciplinary manner constitutes planetary humanities: a field that studies connections between humans and planet earth. The course engages with diverse areas such as phenomenology, ontology, astronomy, philology, Earth System Science, and the Gaia hypothesis. The course encourages students to think creatively about the earth and emphasizes understanding how the earth has been conceptualized in philosophy, history, and science before and after the Copernican revolution.

Building on the problem framing and viability assessments from the previous semesters, this course transitions students from ideation to execution. Students move beyond evaluating solutions and begin developing functional prototypes that address their identified grand challenges. The course introduces structured methodologies for iterative design, rapid prototyping, and user testing, equipping students with the tools to translate concepts into tangible, testable artefacts. Students engage closely with end users and communities to gather feedback and refine their prototypes through multiple cycles of iteration. Emphasis is placed on technical rigour, interdisciplinary collaboration, and the ability to communicate design decisions to both technical and non-technical audiences. The semester culminates in a prototype demonstration where teams present their solution, the design journey, and key learnings from user feedback.

Building on the iterative prototyping work of ILGC 4, this course guides students through the critical transition from a working prototype to a solution that is viable, scalable, and ready for real-world deployment. Students stress-test their solutions against the complexities of actual implementation, accounting for resource constraints, stakeholder ecosystems, regulatory considerations, and long-term sustainability. Implementation roadmaps are developed alongside pathways for scale, including social enterprise models, policy engagement, and technology transfer, with collaboration encouraged with external partners, NGOs, industry stakeholders, or government bodies where relevant. The course also introduces frameworks for measuring social impact, enabling students to articulate the tangible outcomes and broader significance of their work.

The final course in the ILGC series brings the student's four-year grand challenge journey to a close. This semester is centred on reflection, communication, and impact. Students will consolidate their project work into a comprehensive showcase: - documenting the full arc of their journey from initial observation to implemented or deployable solution, and articulating the social, technical, and personal dimensions of their learning. Beyond project completion, students are encouraged to reflect critically on what worked, what did not, and what they would do differently - developing the habit of structured retrospection that defines thoughtful engineering practice. The course also situates students' individual projects within the broader landscape of the SDGs and global grand challenges, helping them understand the systemic context of the problems they have engaged with. The semester culminates in a public exhibition where students present their work to faculty, peers, and external stakeholders, marking the completion of their ILGC journey.

Professional Communication equips students with critical communication competencies, enabling them to present themselves professionally to diverse audiences, including industry recruiters and higher education admission committees for BTech engineering students. Four key areas are covered: building a professional portfolio, delivering impactful technical presentations, mastering interview strategies, and developing networking skills. These competencies enable students to communicate with clarity, confidence, and impact across academic and professional contexts. The course complements Plaksha's engineering curriculum, preparing students for successful engagement with both industry and academia.

The Research Communication course introduces undergraduate engineering students to the foundational skills, methods, and processes of academic research writing. Students learn to abstract research themes from real-world problems, identify themes within research literature, conduct a critical literature review, identify research gaps and articulate research questions, and formulate hypotheses supported by evidence. The course culminates in a full-length research review manuscript that conforms to academic norms and engineering writing standards.

Why do stories fascinate, and how do they shape personal and cultural identity? Questions of kinship, power, gender, locality, and the relationship between past and present are formulated and expressed differently across narrative genres, carrying social, political, religious, historical, and personal themes. This course examines a variety of narrative forms in India, oral and written, traditional and modern, including regional folk tales, sung and performed oral epics, songs sung by women about women, ancient myths, medieval historical legends, contemporary biographies, feature films, and modern prose and verse.

Theories of orality and literacy, spoken word and visual image, ritual, theatre, and performance deepen this exploration, building toward an understanding of how culture, society, and history are constructed, enacted, and represented through narrative in India and beyond.

Understanding the customer is the starting point, but product market fit is the breakthrough. This course demystifies what product market fit means and why it is essential for any venture, helping students define the right problem, identify the right customer, and design business models that deliver real value. Through case studies, founder sessions, and workshops, students learn to turn ideas into solutions customers care about, building the mindset and tools to move forward with clarity, confidence and traction.

Equipping undergraduate engineering students with the ability to explain science and technology clearly, accurately, and meaningfully to stakeholders outside their field, this course addresses a growing need: engineers today are expected not only to build solutions but also to communicate their ideas to policymakers, industry leaders, investors, communities, and everyday users. Technology achieves lasting impact only when society understands and trusts it, making science communication essential for every engineer.

Students learn the principles and practice of communicating scientific and technical concepts to public audiences, developing skills to simplify, narrate, and adapt complex information into engaging, scientifically accurate content. Unlike traditional academic writing, the course emphasises creativity, storytelling, structure, and clarity.

Frameworks and tools for understanding pressing societal problems and challenges anchor this course, along with how these challenges can be addressed through entrepreneurial ventures that often leverage technology to create impact at scale. Causes that affect society at large, such as education, health, agriculture, and climate, are unique in their complexity and need to be approached, analyzed, and tackled differently than conventional venture-building spaces. Students examine how social ventures are built, and why they succeed in creating impact at scale or not.

Through examples and case studies, students learn to deeply understand a social problem, identify the broader systemic drivers behind it, incorporate field and market research and stakeholder mapping into potential solutions, and develop and test pilots and methods for assessing both quantifiable and non-quantifiable outcomes.

Designed for students who have completed the Entrepreneurial Challenge Lab and are ready to turn validated concepts into scalable ventures, this course translates entrepreneurial theory into actionable strategies, covering refining value propositions, go-to-market planning, financial modeling, valuation and funding pathways. A distinctive feature is hands-on learning with successful startup founders, who lead sessions on topics including growth hacking, leveraging technology for scale, and building compelling pitch decks. These insights are reinforced through workshops, practical exercises and investor pitch sessions.

Rising incomes coupled with rapid advances in agricultural and food technologies have driven dramatic improvements in food production and global food and nutrition security over the past six decades. Growing concerns persist, however, over the sustainability of the global food system, driven by depletion of natural resources, deterioration of ecological systems, extreme weather events, and an epidemic of chronic diseases. More than 700 million people suffer from hunger, and approximately 2 billion are deficient in one or more micronutrients. Between 1 and 2 billion people suffer from water scarcity or lack access to safe drinking water. Meanwhile, the world remains largely dependent on fossil fuels as its predominant energy source, generating greenhouse gas emissions that drive climate change. The US, China, and India account for more than 40% of the world's population and collectively consume approximately 50% of global food, energy, water, and natural resources. As the largest developed and developing countries, respectively, these three nations have substantial and sustained impacts on the ability of the rest of the world to address existing and emerging challenges in the global food, energy, and water nexus. They share many common interests while facing distinct differences and challenges in meeting their needs for food, energy, water, and economic development. This interdisciplinary course is offered by Cornell University, with joint efforts from two leading agricultural universities in China (China Agriculture University and Nanjing Agricultural University) and the Tata Institute of Social Sciences (TISS), a premier institution offering undergraduate and graduate degrees in social sciences in India. The University of Arkansas joined the collaboration in Fall 2020, Hefei University of Technology (Hefei, China) joined in Fall 2022, and the University of Puerto Rico-Mayaguez joined in 2023. Plaksha University in India joined the collaboration this year.

This course focuses specifically on the nuances of venture investing at both early-stage and late-stage. Students will learn to navigate diverse venture investment scenarios, including seed funding, Series A investments, and pre-IPO rounds. Through practical exercises, case studies, and interactive workshops, participants will develop comprehensive skills in deal evaluation, due diligence, financial analysis, and strategic decision-making tailored for venture capital. This course serves as a continuation of the Entrepreneurial Finance course offered in the fourth semester, building upon foundational financial concepts.