Instructor Profile:
Dr. Nirmal Halder
Assistant Professor, Aerospace Engineering, Sandip Institute of Engineering & Technology, Nasik
Dr. Nirmal Halder serves as an Assistant Professor in the Aerospace Engineering Department at Sandip Institute of Engineering & Technology, Nasik. With a solid academic foundation, he obtained his Bachelor of Technology (B.Tech) degree from Jalpaiguri Government Engineering College. He furthered his studies by earning a Master of Technology (M.Tech) from the prestigious Indian Institute of Technology (IIT) Guwahati, followed by a Doctorate (Ph.D.) from IIT Kanpur.
Dr. Halder’s research primarily concentrates on heat transfer, a crucial area within aerospace engineering. His scholarly contributions are notable, having published five papers in reputable journals, all indexed by respected databases. These publications underscore his commitment to advancing knowledge in the field and contribute significantly to the academic community.
In his role at Sandip Institute of Engineering & Technology, Dr. Halder is dedicated to fostering academic excellence and mentoring the next generation of engineers. His expertise not only enhances the learning environment but also promotes innovative research that aligns with the evolving demands of aerospace engineering.
Dr. Halder’s passion for education and research makes him a valuable asset to both his students and the broader academic community, as he continues to inspire future leaders in aerospace engineering.
Course Name: Association Rules & Regression
Module 1: Introduction to Data Mining and Association Rules
- Overview of Data Mining: Definition and significance of data mining, Key concepts and techniques in data mining
- Association Rules: Understanding the concept of association rules, Applications of association rules in various domains
- Key Algorithms for Association Rule Mining: Apriori algorithm, FP-Growth algorithm
- Evaluation Metrics for Association Rules: Support, confidence, and lift, Techniques for pruning rules
Module 2: Advanced Techniques in Association Rules
- Handling Large Datasets: Techniques for efficient association rule mining, Scalability issues and solutions
- Sequential Pattern Mining: Introduction to sequential patterns, Algorithms for sequential pattern mining (e.g., GSP, PrefixSpan)
- Constraint-Based Association Mining: Incorporating constraints in association rule mining, Applications of constraint-based mining
- Real-World Case Studies: Analysis of real-world datasets, Implementing association rules in practical scenarios
Module 3: Introduction to Regression Analysis
- Understanding Regression: Definition and importance of regression analysis, Different types of regression (linear, multiple, polynomial, etc.)
- Simple Linear Regression: Assumptions of linear regression, Model building and interpretation of coefficients
- Multiple Linear Regression: Extending linear regression to multiple variables, Model diagnostics and evaluation
- Applications of Regression Analysis: Use cases across various fields (e.g., finance, healthcare)
Module 4: Advanced Regression Techniques and Model Evaluation
- Polynomial Regression and Regularization: Concepts of polynomial regression, Techniques such as Lasso and Ridge regression
- Logistic Regression: Understanding logistic regression for binary classification, Interpretation of logistic regression outputs
- Model Evaluation and Selection: Evaluation metrics (R-squared, RMSE, AIC, BIC), Cross-validation techniques
This course is designed to equip learners with a comprehensive understanding of association rules and regression techniques, providing them with the skills necessary to analyze data effectively and derive actionable insights.
