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This session addresses how generative AI is reshaping data science education by enabling unprecedented levels of personalized, adaptive, and inclusive learning experiences. It explores the ways in which AI-driven tools support the customization of instructional content, allowing students to learn at their own pace and according to their unique preferences, thereby moving beyond the traditional one-size-fits-all model of education.

This session covers the overall architecture of AI Tutor applications.

In this session the class will review and discuss designs for the tutor applications.

This session covers Apache Spark, its architecture, and various components, providing a comprehensive overview of the unified analytics engine.

The session will cover the basics of execution in Apache Spark, starting with an explanation of SparkContext, which is responsible for creating a Spark environment. The concept of Directed Acyclic Graphs (DAG) will be introduced, as it plays a crucial role in optimizing Spark’s execution plan.

This session covers the essential aspects of using Apache Spark for machine learning tasks. We discuss the foundational components that enable efficient and scalable processing.

This session covers the fundamental aspects of graph analytics and its application in various domains. We discuss the introduction to graph analytics, highlighting real-world use cases where graph analysis provides valuable insights.

This session introduces the foundational concepts and practical tools used in digital image processing as they relate to data analytics.

In this session, students move beyond basic image manipulations to the extraction of informative features and their use in classical machine learning models.

This session introduces students to deep learning approaches that have revolutionized computer vision tasks.

This session explores the rapidly advancing domain of generative models and their applications in computer vision analytics.

This hands-on worksession provides an opportunity for students to engage with one another’s projects, fostering a collaborative learning environment. As students work on their individual solutions, they meet to discuss their progress, share ideas, and exchange assistance. The session encourages peer-to-peer interaction, allowing participants to leverage each other’s skills, insights, and perspectives. By sharing knowledge and expertise, students can enhance their understanding of the subject matter while developing valuable collaboration and communication skills.

This hands-on worksession provides an opportunity for students to engage with one another’s projects, fostering a collaborative learning environment. As students work on their individual solutions, they meet to discuss their progress, share ideas, and exchange assistance. The session encourages peer-to-peer interaction, allowing participants to leverage each other’s skills, insights, and perspectives. By sharing knowledge and expertise, students can enhance their understanding of the subject matter while developing valuable collaboration and communication skills.

See AI Tutor Project page…