Data Science and Artificial Intelligence – Leadership Essentials

Last Day for Registration : April 22, 2024

SKU: IIT Madras | Start Date: 1st May, 2024 Categories: ,

Course Description

Download the Brochure

About The Course:

Centre for Outreach and Digital Education (CODE), IIT Madras in collaboration with Wadhwani School of Data Science and Artificial Intelligence, IIT Madras offers a short-term executive certificate programme on Data Science and Artificial Intelligence – Leadership Essentials. This is a 2 month course that aims to give a high level overview of machine learning/deep learning along with deployability and responsibility aspects of AI.

The course will include case studies that will help managers appreciate the practical applications of the algorithms and how to effectively translate to their work/domains of interest. The goal is to keep the course less mathematical and at a high level. In the course, there will be TA driven walkthroughs of code for key algorithms discussed.

Mode of Course: 20 hours of recorded videos and 10 hours of online live interactive sessions with the faculty

Modules Covered:

Module 1 : MATHEMATICAL FOUNDATIONS

Module 2: SUPERVISED LEARNING -1

Module 3 : SUPERVISED LEARNING – 2

Module 4 : UNSUPERVISED LEARNING

Module 5 : DEEP LEARNING-1

Module 6: DEEP LEARNING-2

Module 7 : DEPLOYABILITY

Module 8: GENERATIVE AND RESPONSIBLE AI

Click on the tab “Module Description” and choose the appropriate module from the dropdown menu to view more information about each module.

Register before April 1, 2024 (04:00 p.m.) to avail a discounted price of Rs. 59,000 (Rs. 50,000 + 18% GST)

Course Start Date: May 1, 2024

Mode: Pre recorded Videos & Assignments will be released on Fridays and Online Live session on Saturdays.

Last date of Registration:  April 26, 2024

Profile of the Instructor(s)

Prof. Sridharakumar Narasimhan, Department of Chemical Engineering, IIT Madras

Prof. Nandan Sudarsanam, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Prof. Balaji S. Srinivasan, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Prof. Ganapathy Krishnamurthi, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Prof. B. Ravindran, Head – Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Module Description

MODULE – 1 : Mathematical Foundation

Faculty:

Prof. Sridharakumar Narasimhan, Department of Chemical Engineering, IIT Madras

Concepts Covered:

  • Data representation as vectors, matrices
  • Discovering relationships in data
  • Descriptive statistics
  • Predictive statistics

Learning Outcomes:

• Represent data as vectors and matrices

• Manipulate data through vector and matrix operations

• Test for relationships between different variables

• Apply simple data compression tools

• Compute fundamental statistical quantities and understand their importance for data
• Compute basic probabilities from data for predictive modelling

MODULE – 2 : Supervised Learning 1

Faculty:

Prof. Nandan Sudarsanam, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered:

  • Overview of supervised learning
  • Regression Analysis
  • K-Nearest Neighbours algorithm
  • Bias-Variance dichotomy
  • Regularization
  • Model Validation

Learning Outcomes:

  • To get introduced to the basics of supervised learning.
  • To analyse different types of data and prediction problems with its practical applications based on the output variable.
  • To understand techniques for regression problems, including Regression analysis (OLS) and K-Nearest Neighbours.
  • To gain familiarity with advanced concepts like Bias-Variance dichotomy, Regularization, and Model Validation with a goal of improving the performance of models.

MODULE 3 : Supervised Learning – 2

Faculty:

Prof. Nandan Sudarsanam, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

  • Introduction to classification
  • Logistic Regression
  • Linear Discriminant Analysis
  • Support Vector Machines
  • Decision Trees
  • Ensemble methods – Bagging, Boosting, Random Forests

Learning Outcomes:

  • To understand the differences between classification and regression problems, and the need for separate models for classification.
  • To understand techniques for classification problems, including Logistic Regression, Linear Discriminant Analysis and Support Vector Machines.
  • To learn techniques such as Decision Trees and Ensemble methods which can be implemented for both regression and classification problems.

MODULE 4 : Unsupervised Learning

Faculty:

Prof. Balaji S. Srinivasan, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

  • Overview of Unsupervised Learning
  • Clustering Techniques – K-Means, Hierarchical Clustering, DBSCAN
  • Dimensionality Reduction – PCA, t-SNE
  • Association Rules
  • Anomaly Detection
  • Overview of Neural Networks for Unsupervised Learning

Learning Outcomes:

  • Primary Outcome – Ability to identify appropriate use-cases for and challenges in various unsupervised algorithms in a business context.
  • How unsupervised learning differs from supervised learning.
  • Key concepts and terminology in unsupervised learning.
  • Understand the basics and applications of different clustering algorithms.
  • Understand the concept of dimensionality reduction and why it is important in handling high-dimensional data.
  • Learn how dimensionality reduction can be applied to simplify datasets for easier analysis and visualization.
  • Understand the concept of association rules and their significance in discovering relationships in data.
  • Gain insights into how association rules can drive business strategies like product placement and inventory management.
  • Understand what anomaly detection is and its importance in various domains like fraud detection, network security, and industrial defect detection.
  • Basic understanding of how neural networks can be applied in unsupervised learning.

MODULE 5 : Deep Learning 1

Faculty:

Prof. Balaji S. Srinivasan, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

  • Overview of deep learning
  • Basic principles and terminologies in neural networks.
  • The Neural Network Architecture
  • The backpropagation algorithm
  • Convolutional Neural Networks (CNNs) for Vision.
  • Recurrent Neural Networks (RNNs) for Sequential Data
  • Key Ideas in Deep Learning: Regularization, Embeddings, Transfer Learning

Learning Outcomes:

• Primary Outcome – Understand when to use Deep Learning for your applications, what architectures
would be most appropriate, and what the various components of a typical Deep Learning pipeline are.
• Develop an understanding of the evolution and fundamentals of deep learning
• Grasp the mechanics of deep neural networks, including the backpropagation process and
various network architectures.
• Understand the role of CNNs in image and visual data processing, including their applications and impact.
• Recognize the importance of RNNs and LSTMs in handling sequential data.
• Gain insights into key deep learning concepts such as regularization, embeddings, and transfer
learning, and understand their significance in practical applications.

MODULE 6 : Deep Learning 2

Faculty:

Prof. Ganapathy Krishnamurthi, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

Advanced CNN

  • State of the Art CNN Architectures
  • Image Recognition
  • Object Detection

Advanced Sequential Models

  • Recurrent Neural Networks (RNNs)
  • Attention Mechanism
  • Transformer Architecture – Large Language Models

Generative Adversarial Networks (GANs)

Diffusion Models

Learning Outcomes:

• Appreciate practical applications and implications of deep learning in different domains.
• Gain knowledge about various state of the art CNN architectures and their applications like
object detection used in computer vision.
• Understand mechanisms behind image generating models like Dall-E and Stable Diffusion
• Understand transformer architectures that drive LLMs like chatGPT

MODULE 7 : Deployability

Faculty:

Prof. Ganapathy Krishnamurthi, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

Introduction to AI Deployability

  • Challenges in Deploying AI Models
  • Lifecycle of a Deployed Model

Model Serving and Management

  • APIs
  • Cloud Deployment
  • Model Versioning

Monitoring and Maintenance

  • Continuous Monitoring
  • Performance Metrics
  • Updating Models

Learning Outcomes:

  • Understand the complexities and lifecycle of AI model deployment.
  • Learn how to serve and manage AI models in production
  • Grasp the importance and methods of maintaining AI models post-deployment.

MODULE 8 : Generative and Responsible AI

Faculty:

Prof. B. Ravindran, Head – Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Concepts Covered :

  • Intro to Generative AI – Large Language Models and Image Generation
  • Principles of RAI – Transparency; Accountability; Safety, Robustness and Reliability; Privacy and Security; Fairness and non-discrimination; Human-Centred Values; Inclusive and Sustainable development
  • Examples of Ai/ML systems going wrong – bias, robustness, explanations, hallucinations, prompt injection, data leakage, deanonymization, deep fakes, copyright infringement, etc. Examples will be drawn from various incidents
  • Examples of tools for RAI – measuring bias/fairness, explanations (Lime/SHAP/Grad cam), audit mechanisms
  • Regulation landscape – DPDP act (India), GDPR (EU), EU AI act, US presidential declaration

Learning Outcomes:

  • Understand the intuition behind Gen AI models
  • Understand the principles of responsible AI
  • Appreciate the pitfalls of using AI without proper oversight/auditing mechanism
  • Exposure to RAI tools
  • Exposure to Data Protection Laws and AI regulations.

Fees

Course fees:

Rs. 82,600 (Rs. 70,000 + 18% GST)

Certification

The Certificate criteria for this course is as follows:

Total % will be calculated from all 3 categories

( Assignments, Quizzes and Attendance of Live sessions)

Type of certificate that will be issued

75% -100% Successfully completed
50% -74% Completed
25% – 49% Participated
<25% No Certificate

Certificate Template

Intended Audience

Technical/semi-technical managers with a typical experience of 8+ years who are/wish to manage a team of ML engineers. No coding pre-requisite needed.

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