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 24, 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
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
Principles of RAI – Transparency; Accountability; Safety, Robustness and Reliability; Privacy and Security; Fairness and non-discrimination; Human-Centred Values; Inclusive and Sustainable development.
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
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)
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