Data Science and AI
Data Science and AI
Empower your learning with cutting-edge data science and AI skills that shape the future and pave the way for further study and career opportunities.
Course Instructor
Prof. Arun Rajkumar
Duration
8 Weeks
Course Fee
500
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About The Course
Introduction to Data Science and AI is an 8-week course offered by Prof. Arun Rajkumar from the DSAI Department at IIT Madras. The course offers a gentle introduction to the exciting world of AI. By the end of this course, students will learn some of the core concepts that drive AI applications, including:
- What is machine learning?
- What does it mean for a machine to learn?
- How do machines learn?
Course Instructor
Prof. Arun Rajkumar
Assistant Professor, Department of Computer Sciences & Engineering, IIT Madras
Prof. Arun Rajkumar is currently an Assistant Professor at the Computer Science and Engineering department of IIT Madras. Prior to joining IIT Madras, he was a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore for three years. He earned his Ph.D from the Indian Institute of Science where he worked on 'Ranking from Pairwise Comparisons'. His research interests are in the areas of Machine learning, statistical learning theory with applications to education and healthcare.
Content Overview
Course Duration: 8 Weeks
Module 1 (Weeks 1-2): What is AI - General Pipeline
- Data collection/Feature Engineering in various structured/unstructured settings
- Structured:
- Vegetable Market expenditure data
- Iris Dataset
- Unstructured:
- VPixel values as raw representation of Images - handwritten digits data
- Emails -> features based on word count/ TF-IDF
Module 2 (Weeks 3-4): Types of Learning
- Supervised, unsupervised and sequential learning
- Examples for learning
- Unsupervised learning:
- K-means clustering
- Hierarchical clustering
Module 3 (Weeks 5-6): Algorithm
- Supervised learning:
- KNN
- Decision trees
- Neural network
- Training error and performance measures
- Demo of image classification
Module 4 (Weeks 7-8): Learning with Feedback
- Real-world examples - question answering, healthcare, robot navigation
- How feedback affects the setting
- A simple example of online learning - exam question answering with help from friends
- How can one learn when no friend is good enough?
Live Sessions: Weekly live interactive sessions on Saturdays
Assessment Layout
Assignments: The course includes the release of 4 bi-weekly online assignments (every alternate week). Students need to attempt at least 3 of these assignments and must achieve a minimum score of 40 marks in each to be eligible for the certificate.
Take-Home Project: An optional project consisting of questions that cover all the topics from the theory videos, accompanied by a small project report.
Final Exam: An optional online final exam designed to assess the knowledge gained throughout the course, which can only be attempted after completing and submitting the required assignments and the take-home project.
Certificate Acquisition
Participation Certificate - Check Sample Certificate
To receive this participation certificate, a student must pass 3 out of 4 assignments with an average score of at least 40 marks.Completion Certificate - Check Sample Certificate
To receive this completion certificate, a student must fulfill the following criteria:
- A student must pass 3 out of 4 assignments with an average score of at least 40 marks.
- Submission of the take-home project is mandatory for the student.
- Once the above two conditions are satisfied, the student should attempt the final exam and must receive a minimum score of 40 marks.