This course provides a foundational and practical introduction to Python for AI and data science applications. Students will learn core programming concepts, including data structures, control flow, functions, and object-oriented programming. Special focus is given to data manipulation, visualization, and automation using libraries like NumPy, Pandas, and Matplotlib. Hands-on projects will reinforce concepts through real-world scenarios in AI, finance, and business analytics. Students will also explore debugging techniques and best practices for writing efficient code. By the end, learners will be proficient in Python and ready for advanced AI coursework.

This foundational course grounds students in the essential mathematical concepts that underpin machine learning and data science algorithms. It covers multivariable calculus, focusing on partial derivatives, the chain rule, Jacobians, and gradients to explain backpropagation and loss optimization. Core linear algebra concepts—including vector spaces, matrix operations, determinants, eigenvalues, eigenvectors, and dimensionality reduction via SVD/PCA—are linked to data representations. Foundational probability theory and statistics explore continuous random variables, common probability distributions, Bayes' Theorem, and variance to model data uncertainty. The course also introduces continuous optimization principles, contrasting convex versus non-convex landscapes and cost function minimizations. Together, these modules enable students to analyze, derive, and implement modern ML algorithms with mathematical rigor.

AR/VR (Augmented Reality/ Virtual Reality) Lab particularly focuses on interaction technologies including virtual, augmented and mixed reality as well as mobile computing, epigenetic and evolutionary robotics, and haptic communication.

This lab provides exposure to students to the complexity, breadth, and depth of how vision works and how to make machines “see the way humans do” in different areas including healthcare, agriculture, retail, autonomous vehicles, drones, manufacturing, security, traffic management, remote sensing, defense applications, and so on.

Media Studio is designed with state of the art equipment used in newsrooms for students to gain extensive practice in the technology and process of content creation. Students will get hands-on training in the audio, video, and social construction of media and the associated best practices in audience research.

Production Control Lab will complement the media studio to mimic the environment and technological standards of the live creation of digital media and longer feature programming. Students will become skilled at lighting design, reading from a teleprompter, managing multiple audio and video inputs, in-ear communication and monitoring, robotic and person-operated cameras, pacing and voice, and collaborative construction of media at this lab.

Video Editing Bay includes state-of-the-art audio equipment and is also be outfitted for HD, Ultra-HD, and 4K video editing including Adobe Creative Suite software, a playback interface, a color console, and a professional reference monitor. In these rooms, students will be able to refine and edit video shorts, news pieces, feature-length stories, documentaries, and AR/VR content.
 

Audio Editing Bay comprises acoustically treated rooms that will be equipped with sound-mixing consoles, associated software, surround sound, and amplification. These rooms will allow students to record and edit podcasts, and voice-overs for films and AR/VR applications.

This course covers essential mathematical concepts that form the backbone of AI and machine learning. Students will explore probability theory, statistical inference, and key linear algebra techniques used in data representation and model computations. Emphasis is placed on eigenvalues, matrix factorization, and vector spaces, crucial for optimization and deep learning. The course includes practical applications in AI and real-world problem-solving. By the end, learners will develop a strong mathematical intuition for AI algorithms. Hands-on exercises reinforce theoretical concepts through Python-based implementations.

This course provides a foundational and practical introduction to Python for AI and data science applications. Students will learn core programming concepts, including data structures, control flow, functions, and object-oriented programming. Special focus is given to data manipulation, visualization, and automation using libraries like NumPy, Pandas, and Matplotlib. Hands-on projects will reinforce concepts through real-world scenarios in AI, finance, and business analytics. Students will also explore debugging techniques and best practices for writing efficient code. By the end, learners will be proficient in Python and ready for advanced AI coursework.

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in programming languages. This will help you understand what is going on inside a particular built-in data structure implementation and what to expect from it. You will also learn typical use cases for these data structures. In this course, we will explore several fundamental algorithms and data structures in computer science. Some of the data structures, we will encounter include linked lists, stacks, queues, trees, heaps, hash tables, and graphs. We will study and analyze algorithms for searching, traversing trees, hashing, manipulating priority queues, sorting, finding shortest paths in graphs, and much more.

The basic idea of this course is to help you understand many of the fundamental data structures of computer science. With an appreciation for data structures and algorithms and practical experience in implementing them you can be a much more effective designer and developer for new applications. Elegant algorithms are also a nice counterpoint to the crafty code and weird features we encounter in daily work.

The ability to deal with data plays a critical role in all disciplines of information technology. This course covers all the essential topics in database management building the foundation needed for dealing with persistent data. It introduces the fundamental concepts of designing, using, and implementing database systems and applications.

Database system architectures, Relational data model, Query Processing, Database design, Data Storage and Indexing, Query Optimization, Transaction Management, Data Warehousing, Data Analytics, Advanced Topics: Data Mining, Blockchains, etc.

The applicability and use of optimization and decision models have increased significantly in recent years.  Information has become an important organizational resource, and optimization models play an important role in the optimal utilization of this resource along with others, in the form of organizing and structuring information so that it can be used more efficiently. This course will introduce and apply optimization techniques to managerial decision problems with the objective of enhancing decision-making capabilities as well as knowledge about using spreadsheet-based tools.

The course emphasizes model-formulation skills and provides a basic understanding of some of the assumptions and limitations of decision models. We will not focus on algorithmic details but will use established software packages to obtain the solutions.  The major emphasis is on interpreting the solution and strategizing based on the interpretation.  The aim of the course is to help students become intelligent consumers of analytical modeling tools and adopt an analytical, data-driven approach to making business decisions.

Data contains a wealth of insights about the process that generated it. Finding deep insights from data by asking it the right questions, interpreting the answer to those questions correctly, and letting the data speak to us is an essential skill for a data scientist. 
There is data all around us. As humans, to make sense of the world and the information that it contains, we rely on our visual systems and cognitive abilities. Well-designed information visualizations leverage these human capabilities to help see and understand data. In this course, we will study techniques for understanding and creating effective visualizations based on principles from perception, design, and cognitive science. The course is targeted toward students interested in understanding effective visualizations, building better visualization tools and systems, as well as understanding the state of the art in research in this space.
 

Data obtained from observations collected sequentially over time are extremely common in a business requiring analysis of time series data. Some examples are weekly interest rates, daily closing stock prices, monthly price indices, yearly sales figures, etc. In addition, time series analysis is applicable in diverse fields.  Much familiar time series occur in the field of economics, where we are continually exposed to daily stock market quotations or monthly unemployment figures, annual GDP figures, etc.  Social scientists follow population series, such as birthrates or school enrollments. An epidemiologist might be interested in the number of Covid - 19 cases observed over some period.

The first step in any time series investigation involves scrutiny of the recorded data plotted over time. This scrutiny often suggests the method of analysis as well as statistics that will be of use in summarizing the information in the data. The time domain approach which assumes that correlation between adjacent points in time is best explained in terms of a dependence of the current value on past values. This focuses on modeling some future value of a time series as a parametric function of the current and past values, usually by decomposing components of time series data.  Another approach develops a systematic class of models called autoregressive integrated moving average (ARIMA) models to handle time-correlated modeling and forecasting, with a provision for treating more than one input series through multivariate ARIMA. A completely different approach to time series analysis is the survival analysis. The objective of this course is to expose students to various methods of analyzing time series data to understand its behavior as well as make forecasts for the future.

Traditional database systems and data analysis algorithms have become increasingly insufficient to deal with this unprecedented growth in the volume, variety, velocity, and veracity of data. Big Data tools and techniques promise to store and crunch this data at scale, find deep insights in it, build sophisticated prescriptive, predictive, and descriptive models from it, and enable accurate and real-time decisions. Adapting business models to digital through data and analytics is critical for the long-term success of mainstream enterprises.

The objective of this course is to primarily focus on modern database systems to solve real-world business problems for both batch and streaming data. This course aims to take a 360-degree view of the Big Data ecosystem through various prisms i.e., start-ups, VC firms, publicly traded, and privately held corporations. The course is going to be a good mix of theory, hands-on experience, and class discussions on both case studies as well as business use cases. 

Most modern Enterprises across variety of verticals that have become “data rich” due to the first wave of digital transformation (Automation) are now investing heavily to become “AI First” in their second wave of digital transformation. They are aspiring to migrate from broadcast, batch, reactive, tactical decision-making to personalized, real-time, proactive, strategic decision-making. This has the potential to improve the quality of service they provide to their customers, the efficiency and reliability of their operations, optimal utilization of their resources – personnel, money, and materials, and adapting their ability to make dynamic decisions with evolving contexts. This in turn leads to systemic growth in customer satisfaction, net profitability, and market share.

This unprecedented paradigm shifts in “how Enterprises make decisions” is enabled by a proven suite of algorithms, techniques, frameworks, processes, and platforms that have emerged in the varied fields of Artificial Intelligence, Machine Learning, Data Mining, Statistical Pattern Recognition, etc. over the last several decades.

In this course, we will study a plethora of frameworks, algorithms, techniques, tools, and guiding principles to (a) understand any type of data better, (b) combine it with the domain knowledge appropriately, (d) find deeper insights hidden in the data, (d) build a wide variety of powerful descriptive, predictive, and prescriptive models, and (e) continuously improve them with feedback data coming from the environment they are deployed in.

Language can be considered our “sixth sense” that has literally made humans the dominant species on the planet and made modern human civilization possible. It is the primary mechanism used throughout human history to communicate and share knowledge both across society and across generations. One of the holy grails of AI is to build Machines that can “understand” (analyze), “generate” (synthesize), and “translate” language the way humans do so that we can communicate with machines in our natural languages the way we communicate with each other.

This course introduces the major challenges of, and classic approaches to, Natural Language Processing (NLP) at a beginning graduate level.  The course takes a functional approach — developing data representations and algorithms based on analysis of what is required by key uses of language — as well as considering the critical properties of language with respect to these uses.  The primary focus is on developing applications to enable computer use of language as a medium of communication with human beings.

This course delves into the evolution of computer vision, from classical image processing to deep learning-based AI techniques. Topics include feature extraction, object detection, and convolutional neural networks (CNNs) for image recognition. Students will study traditional approaches like edge detection and SIFT before transitioning to modern architectures such as ResNets and Transformers. Case studies on medical imaging, autonomous vehicles, and facial recognition are included. Practical coding exercises will provide hands-on experience with OpenCV and TensorFlow. The course equips students with the ability to design and implement vision-based AI solutions.

Unstructured and semantically low-level data such as images, videos, audio, and text are becoming increasingly common in enterprises and society as people and systems generate and consume such data in both public and private settings. Human systems of visual, auditory, and linguistic perception and generation have mastered the art of processing such data in the brain. Modern Deep learning systems try to mimic a similar multi-layered architecture to interpret this low-level data (e.g., pixels, signals, words) into high-level semantics. 
This course explores the biological, computational, theoretical, architectural, and modelling aspects of deep learning models that come in a variety of architectures, on different data types and are used for a variety of applications.

“With great power comes great responsibility”. AI is emerging as a tool for fundamental and disruptive transformation of human society. If not used ethically and responsibly, it has the potential of doing more harm than good to individuals, societies, and the human civilization as a whole. Strong regulations are emerging across the globe to ensure that AI is used for the right purpose, in the right way, and with the right governance.

In this core course we will understand the emerging guiding principles of Responsible AI, the state-of-the-art algorithms used to detect and correct any intentional or unintentional transgressions by AI systems w.r.t. these guiding principles, and the emerging landscape of regulations for AI across the world. This will enable emerging data scientists to “consciously and proactively” incorporate these principles and regulations into their model building from the start and not as an afterthought.

This course explores Generative AI models and the principles of prompt engineering for effective interaction with AI systems. Topics include the architecture of generative models like GANs, VAEs, and diffusion models, along with practical applications in text, image, and code generation. A deep dive into prompt engineering strategies will enhance students’ ability to guide AI outputs efficiently. The course includes industry use cases such as chatbots, content creation, and AI-assisted design. Ethical considerations and limitations of generative AI are also discussed. Hands-on labs with tools like ChatGPT and Stable Diffusion ensure experiential learning.

This course explores the architecture, training, and applications of Large Language Models (LLMs) like GPT, BERT, and LLaMA. Students will understand transformer architectures, attention mechanisms, and model fine-tuning for domain-specific tasks. The course covers ethical concerns, bias mitigation, and responsible AI practices in LLM deployment. Hands-on labs include building and fine-tuning LLMs using Hugging Face and OpenAI APIs. Use cases in natural language processing, chatbot development, and automated text generation are discussed. By the end, students will be equipped to leverage LLMs for enterprise and research applications.

Machine Learning Operations (MLOps) refers to the tools, techniques and practical experiences required to train your machine learning models and deploy and monitor them in production. After we have trained our machine learning model, the next big task is to deploy the model to production and scale it so that more users can use it. In this course, you will learn how to use various tools and methodologies to do all this effectively.

While knowing machine learning and deep learning concepts is essential, but for building a successful career in Artificial Intelligence, you need to have good experience with production engineering capabilities. This course deep-dives into machine learning and deep learning algorithms along with building expertise in DevOps technologies.

This course explores the strategic role of AI in driving digital business transformation across industries. Students will analyze AI-driven innovation in areas such as automation, customer experience, and data-driven decision-making. Key topics include predictive analytics, AI-powered marketing, and operational efficiencies through intelligent systems. Case studies from finance, healthcare, and retail illustrate real-world applications. Ethical and regulatory considerations in AI adoption are also covered. By the end, students will be equipped with the knowledge to leverage AI for competitive advantage in digital enterprises.

This course examines the evolving AI stack that enables tool-enabled, autonomous agentic systems. Students learn how modern architectures shift beyond static chat interfaces toward goal-oriented agents capable of multi-step planning, reflection, and stateful memory. The curriculum emphasizes tool use and function calling, allowing models to interface reliably with external APIs, databases, code interpreters, and web search engines. Multi-agent orchestration frameworks such as CrewAI and AutoGen are explored to coordinate collaborative workflows and delegated task execution. Special focus is placed on the Model Context Protocol (MCP) as an emerging open standard for secure, modular context exposure between models and heterogeneous client-server environments. Practical labs guide learners through building, evaluating, and deploying production-grade agents equipped with custom tools and persistent memory.

The course introduces the mathematical and algorithmic foundations of decision-making agents interacting with uncertain environments. Formalized through Markov Decision Processes (MDPs), students investigate state transitions, reward formulations, Bellman equations, and the exploration-exploitation tradeoff. The curriculum moves systematically from tabular value-based approaches like Q-learning and SARSA to deep function approximation using Deep Q-Networks (DQN) with experience replay. Learners study policy-gradient fundamentals, including REINFORCE, and implement Actor-Critic architectures (A2C/A3C) for continuous action spaces. Advanced modules cover Proximal Policy Optimization (PPO), multi-agent reinforcement learning (MARL), and reward design strategies. Students implement simulations across standard gym environments, gaining practical skills to apply RL to robotics, automated trading, resource scheduling, and game playing.

This course provides a comprehensive foundation in cloud architectures, distributed infrastructure, and modern deployment paradigms. Students examine core service models (IaaS, PaaS, SaaS) and deployment patterns spanning public, private, and hybrid enterprise clouds. The curriculum covers compute virtualization, software-defined networking, identity and access management (IAM), and distributed storage systems like block, file, and object stores. Learners explore containerization with Docker, orchestration via Kubernetes, and serverless compute models to design resilient, fault-tolerant workloads. Practical emphasis is placed on cloud-native design principles, elasticity, auto-scaling, disaster recovery, and cloud cost optimization frameworks. By course end, students are equipped to architect, secure, and manage scalable cloud infrastructure supporting modern enterprise applications and high-throughput data processing systems.

This forward-looking course introduces quantum computing principles and their potential to transform machine learning and optimization. Students explore quantum mechanics fundamentals, including qubits, superposition, entanglement, measurement postulates, and quantum circuit operations. Foundational algorithms such as Grover’s search and Shor’s factoring are analyzed to illustrate theoretical quantum speedups over classical algorithms. The syllabus examines core Quantum Machine Learning (QML) concepts, covering parameterized variational quantum circuits (VQCs), quantum kernels, and Quantum Support Vector Machines (Q-SVM). Hands-on exercises utilize frameworks like Qiskit or Cirq to simulate quantum algorithms while addressing current NISQ-era hardware limitations and noise. By the end of the course, learners understand hybrid classical-quantum paradigms and their applications in combinatorial optimization and molecular design.

●    AI for Telecom
●    AI for Retail
●    AI for Manufacturing
●    AI for Refineries
●    AI for Banking
●    AI for Finance
●    AI for Insurance

●    AI for Agriculture
●    AI for Healthcare
●    AI for Education
●    AI for Smart Cities
●    AI for Sustainability

●    AI for Customer Lifetime Management
●    AI for Human Resources 
●    AI for Accounting and Finance
●    AI for Infrastructure and Asset Management
●    AI for Supply Chain and Operations
●    AI for Risk and Compliance

“Necessity is the mother of invention”. Every successful product that we use today is addressing a very critical human need, addressing an important individual, industrial, or societal pain point, and has evolved over multiple iterations through a very well defined process that has now matured into what we call “Design Thinking”. Traditionally, the design thinking process is taught mostly to product managers, customer experience designers, and growth managers. But as the charter of AI/DS is evolving from merely “embedding intelligence into existing products and services” to “building intelligent solutions for society”, it becomes critical for AI architects and Data Scientists to also become first principles “product thinkers” and “design thinkers”. 

This hands-on Holistic Learning Module on Design Thinking will explore the core guiding principles and a systematic process of both conceiving new, and continuously improving existing products by identifying the right pain points and addressing them with the right product features. 

Over the last several centuries, products have evolved from mostly physical products such as cars and TV’s to purely digital products such as search engines and social networks. The next generation products will be complex digital-physical ecosystem products such as telecom networks, e-commerce platforms, agriculture and healthcare for a country, smart cities of the future, and so on. Today these third generation products are put together by clobbering together existing building blocks giving a very siloed experience to customers, a broken architecture to engineers, and a fragmented and inefficient design of operations. A whole new set of skills are needed to conceive, design, build, and operate such ecosystem products that will require a new kind of integrated operating system that doesn't exist yet. 

The Holistic Learning Module on System and Solution Thinking will develop the required skills to not just build and deploy bottom-up AI/ML models wrapped in microservices but to build a top-down AI + IT architecture for such complex ecosystems.

Data Scientists and AI experts not only have to create intelligent solutions for businesses but they also have to become well versed in the art of domain, business, technical, and engineering communication. As they have to work with multiple stakeholders in the ecosystem including the business leadership that articulates the problem statements in business terms, subject matter experts that have built up enormous domain knowledge within the vertical over the years, data and engineering teams that will help them build and deploy the AI solutions at scale, and the operations team who will execute the decisions generated by the AI systems. 

This Holistic Learning Module will explore the art of listening and conversing about pain points that AI/DS can address, domain knowledge required to build the right models and solutions, presenting the insights generated from the data, and describing the nuances of the solution to business, platform, and operations stakeholders. 

There are three different types of leadership and entrepreneurial opportunities for data scientists and AI experts in the foreseeable future. First, leading the transformation journeys of existing businesses in different verticals such as banking, finance, insurance, manufacturing, retail, telecom, mobility, entertainment, etc. from “data-rich” organizations to “AI-first” organizations. Second, building startups that champion an AI-first solution to industrial or societal use-cases addressing the efficiency, precision, personalization, automation, and democratization of AI in these areas, and third, to create completely new business or technology innovations that could disrupt the way we live, work, educate our children, take care of our health, grow our crops, or run our cities and governments. 

This holistic learning module will explore the opportunities, challenges, and a journey towards a “Data Science Leader and Entrepreneur”.