In this course, we will rigorously enquire into the different meanings of the idea of technology and its relationship to society from the perspectives of philosophy, history, social anthropology, human evolution, and civilizational studies. We will look at examples from the past and present, but more importantly, also start imagining what the relationship between technology and society could be in the future. We will ask a number of questions pertinent to this inquiry: Does technology influence society, or, does society influence technology? What indeed is technology? Is technology perhaps first and foremost an ‘idea’? Or, is technology a particular way of knowing the world around us? If it is an idea then it must involve thinking of some kind. But clearly it also involves making something – usually an object that does something or has a particular function. In this course we will begin to understand technology from the standpoint of the threefold matrix of thinking, knowing, and making.

This is an introductory level UG course on probability and statistics. Topics include conceptual introduction to probability axioms, conditional probability, Bayes' theorem, law of total probability and expectation, measures on central tendency and dispersion, probability distributions, simple discrete and continuous probability distribution models, elementary concepts on discrete and continuous time Markov processes, least squares regression analysis, sampling distributions and elementary ideas on hypothesis tests. Students will have to complete an end-of-term project on a topic of their choice.

Building on the Python course taught in the previous semester, students will be introduced to the Object Oriented Programming (OOP) paradigm and the associated benefits. They will learn to write structured and efficient programs in the OOP style. Besides testing and debugging programs, students will learn about common data structures, and where and how to apply them to solve computational problems.

This course introduces students to how nature’s machines work. It covers various aspects that relate the design of living matter/things to engineering. We will discover how to think about: a) the human cell as a factory, the circulatory system as a transport network, musco-skeletal system as a structural network for load-bearing, magnetoreception in animals as a communication system, among other topics. b) Aspects of biomedicine such as the design of impedimetric sensors, digital health, and tissue and genetic engineering. c) Diversity and dynamics of nature pertinent to engineering through several demo examples and recent state-of-the-art applications.

The course introduces core microeconomic models of consumers, firms, and markets, and develops their application to a broad range of economic and social issues in the real world. It will cover concepts of equilibrium, markets and competition, market demand and market supply, behavior of consumers and producers, consumer and producer theory, pricing, tax incidence, making choices under uncertainty, economic efficiency, etc., in the context of contemporary real-world applications around us such as Uber surge pricing, telecom price wars, e-commerce models, recent ed-tech acquisitions, etc.

The advent of technology since the turn of the century has led to many advancements in the way that humans live and operate. This progress, however, also comes with apprehensions, uncertainty, and questions regarding the ethical considerations associated with the owners, regulators, and users of the technology. In this course, we will examine the manifold ethical issues surrounding the use and development of AI, Robotics, Biology, and Business based technologies in the contemporary world. Students will be introduced to ideas of safety, privacy, regulations, and related consequences of bias, manipulation, and fairness, from a multidisciplinary perspective. Students will be introduced to ideas from traditional philosophical ethics texts before delving into real world case studies focussing on contemporary issues.

This course will introduce foundational ideas on mathematical transforms like Laplace and Fourier transforms. Additionally, there will be a two month long module on partial differential equations and vector calculus. In addition to lectures, students will participate in a semester long, state of the art physical laboratory immersion program which will involve training and tinkering with experimental mathematics, such as profiling heat conduction through a metallic rod to understand the mechanics of the heat equation and its solutions, performing fluid mechanics experiments using a rotating tank of water and high-speed camera to collect data and understand the solution states of the Navier Stokes equation, using wavemaker and wave guides to calculate wave velocity and comparing the data with theoretical calculations, performing laboratory experiments with spectrometers to estimate power spectral density of signals and compare the results with mathematical calculations in Fourier space, etc.

This course offers an introduction to the areas of Data Science, Artificial Intelligence, and Machine Learning (DS/AI/ML), combining philosophical, biological, and psychological perspectives with computational design concerns. Students will begin the course by understanding the principles underlying learning and how it translates to various applications and categories of AI. Besides multiple classes of models, students will be introduced to fundamental concepts in data science that will help them identify, compare and implement solutions to problems. Internal critiques and external perspectives of AI solutions will also be discussed to ensure students obtain a holistic view of the challenges and opportunities in this field.

This course introduces the students to the idea of intelligence and how intelligent machines are transforming the world around us. Students will learn about the concept of cyber-physical systems and how intelligence stems from the ‘perception, reasoning, and action loop’. Students will explore several examples of cyber-physical systems in the real world in areas such as robotics, smart grids, and autonomous cars. They will learn about mathematical techniques for modeling cyber-physical systems, how to build intelligent machines by combining sensors, actuators, and embedded devices. Through hands-on lab activities, assignments, projects, as well as through guest lectures spanning research and practice, students will be able to gain the skills to design, build and evaluate simple cyber-physical systems that will give them the confidence to pursue more complex projects in their future endeavors.

This course offers a multidisciplinary overview of the field of optimization. Students will learn how to formulate an optimization problem, apply different optimization techniques, translate problems into Python and Matlab code, and identify conditions under which each one works best. Students will be introduced to numerical, constrained, unconstrained, univariate and multivariate optimization methods and techniques, while also being introduced to topics such as Pareto optimality, multiobjective and global optimization. The final project will allow students to apply their knowledge to real-world engineering and business problems.

Modern computers of all scales rely on efficient algorithms for solving diverse sets of computing problems. Efficient data structures are the backbone of efficient algorithms, guiding the scheme of storage and retrieval of data for speedy and effective processing. This course enables students to recognise the best data structures suited for a given computing problem, design algorithms based on them, and prove bounds on time and space complexity of their algorithms. As part of the course work, the students will also study the implementation and complexity of fundamental algorithms such as max flow in networks, discrete fourier transform, RSA cryptosystem, and real-world systems such as google search engine, and social network graphs.

The advent of deep learning and its application to big data analysis has worked to transform our industries. This course is meant to give students a detailed introduction to the science of deep learning. We undertake a study of various neural network architectures adapted to different problem settings, their training parameters, and algorithms for optimal network performance. We organise the discussion around existing learning paradigms and the specificities of data that guide the network design (via inductive biases). We anchor the study around seminal papers that have, and that continue, to shape the field of deep learning, to give students a flavour for the dynamic nature of the field. As part of the lab work, students will be expected to apply these concepts to real world case studies requiring ML tasks such as scene recognition, speech recognition, image segmentation, etc.

This course will provide students with the tools to use statistical techniques and computational methods to work with data, analyze large datasets, gain insights for specialized problems and ultimately extract meaningful outcomes. Through project work, students will learn all aspects of the data analysis cycle - ranging from what questions to ask, how to devise a data collection strategy, data acquisition and cleaning, understanding data set variables and the relationships between them, data management and storing, processing and analysis of data, summarizing the main characteristics of the dataset, how to discover patterns and spot anomalies, and how to eventually communicate using data.

The course offers practical tools and multiple case studies to understand and analyse the macroeconomy. It will cover issues on measurement of macroeconomic variables, and analysis of the macroeconomy in the short-run and the long-run. Topics in short-run macroeconomics include the monetary system, how central banks set interest rates, the IS curve, the Phillips curve, aggregate demand and supply framework, analysis of the evolution of output, inflation, and interest rates in response to economic shocks and changes in policy. Key models in long-run macroeconomics include the Solow growth model and growth models with ideas. The course will also introduce state-of-the-art DSGE business cycle modelling, dynamic and stochastic dynamic optimization techniques, and applications of AI/ML to growth theory. Real-world applications will include analysis of the great recession and the Covid-19 recession, the European debt crisis, China’s impact on US jobs and wage inequality, income per capita disparities across countries, etc.

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.

In this course, students will learn how data analytics is used in industries ranging from healthcare, e-commerce, sports, social media, etc. by working with a variety of real-world datasets and using concepts of modern algorithmic data analytics. The course will focus on learning methods that are required to analyze large datasets to be able to identify patterns and insights. Students will learn this process through both statistical data analysis tools as well as using computational and algorithmic resources. This course also includes modules that will help students learn mathematical principles, techniques, and applications of data visualization and design, and graphic techniques that are required to create novel and interactive visualizations. The course will culminate with student-led projects that will require the use of analytics, machine learning and visualization on contemporary datasets.

This course introduces mathematical tools to model and analyse situations of interactive decision making. It will introduce concepts of ordinal and cardinal games, solution methods to solving static and dynamic games of complete and incomplete information, social choice theory and voting, mechanism design, auctions, etc. Game theory has found applications in various fields apart from economics such as biology, business, law, politics, sociology, and computer science. Real-life applications and case studies to be discussed include - card games, war strategies, understanding firm behaviour such as price-fixing, collusion, and marketing strategies; voting patterns, restaurant locations, etc.

This course introduces students to the core principles of Management Accounting and Cost Accounting. Students will learn basic accounting concepts of assets, liabilities, double entry system, credits, debits, ledgers and trial balance, and other such topics. Students learn to analyze and interpret financial statements (BS, P&L, Cash flow statements) and key financial ratios, in addition to understanding different costs and costing approaches, cost-volume-profit analysis, and using the analysis to make organizational decisions.

This course will introduce students to basic econometric concepts and methods necessary for conducting empirical analysis. Core topics include simple and multiple regression analysis, hypothesis testing, classical model assumptions, omitted variables, serial correlation, heteroskedasticity, incorrect functional form, and multicollinearity. The course will also highlight the concept of endogeneity and introduce different methods of causal inference such as - randomized experiments, fixed effects and difference-in-differences, and instrumental variables. Topics on time-series analysis will include distributed lag models, dynamic models, forecasting and ARIMA models, etc. Significant part of the course will be devoted to practical application of the course material through hands-on experience using STATA/R.

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.

Reinforcement learning is integral to help us realize the full potential of artificial intelligence. This course introduces students to the domain of reinforcement learning, and how it is applied in areas ranging from autonomous vehicles to advertising. Students will learn the basic principles of reinforcement learning, how to use deep RL algorithms and how to apply deep RL methods to contemporary problems. Given the dynamic nature of the discipline, class discussions will revolve around modern-day applications and innovations in reinforcement learning and challenges posed by it.

This course focuses on the applications of data analytics methods in the field of operations and supply chain management covering areas such as logistics, inventory management and forecasting, operations planning, sales and purchase management, etc. Students will learn to apply data analytics methods and tools, and create models that lead to efficient and effective industry decisions. By the end of the course students will be able to apply analytical methods to topics such as forecasting demand, supply chain risk management, distribution economics, product development, and all stages of the supply chain cycle.

This course is about studying market power and competition, strategic interaction between firms and decision-making by firms, institutions, and industrial policy. It will explore some interesting topics such as pricing strategies, branding strategies, product bundling, asymmetric information, horizontal mergers and vertically integrated markets, antitrust, networks, intellectual property, entry deterrence, etc. The course will introduce multiple economic and game-theoretic models, numerous real life examples and case studies. Some examples of real-life applications that will be discussed include - Airbus vs Boeing and the market for aircrafts, selling used products over Ebay, adverse selection in second-hand car markets, patents in the pharmaceutical sector, reputation on amazon marketplace, expensive popcorn cost at the movies, etc.

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment

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.

This course introduces students to the principles of marketing and how the field has been transformed by the advent of data. Students will learn the fundamentals of marketing analytics, including data modelling and web analytics. Through practical exercises and real-world case studies, students will learn how to leverage data to be able to draw key insights, understand consumer preferences and trends, how analytics can boost marketing and business outcomes, and how data can be used to create marketing strategies and campaigns. Key topics include demand forecasting, market segmentation, revenue forecasting, customer lifetime value, product entry, pricing mechanisms, marketing return on investments, among others.

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment

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.

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment

Sample Electives include: Deep Learning and Computer Vision, Computing at Scale, Negotiation and Analysis, Digital Innovation & Transformation, Large Scale Data Analysis for Public Policy, Economics of Big Data, Computational Finance and Risk Assessment

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

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