Statistical Learning: Advanced Data Analysis Techniques
Master statistical learning methods from regression to neural networks. Gain practical skills in Python for data analysis and modeling.
Master statistical learning methods from regression to neural networks. Gain practical skills in Python for data analysis and modeling.
This comprehensive course offers a deep dive into statistical learning, covering a wide range of techniques from basic regression to advanced machine learning methods. Students will explore linear regression, classification, basis expansion, kernel methods, model assessment, maximum likelihood inference, and advanced topics like decision trees and neural networks. The course emphasizes both theoretical understanding and practical implementation using Python, preparing students for real-world data analysis challenges. By the end, learners will have a robust toolkit for tackling complex statistical problems and interpreting data effectively.
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What you'll learn
Understand and apply various statistical learning techniques
Implement linear regression and classification methods
Utilize basis expansion and kernel smoothing methods
Perform model assessment and selection
Apply maximum likelihood and Bayesian inference
Develop decision trees and support vector machines
Skills you'll gain
This course includes:
5.4 Hours PreRecorded video
37 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 9 modules in this course
This comprehensive course offers an in-depth exploration of statistical learning techniques, guided by the renowned textbook "The Elements of Statistical Learning". Starting with fundamental concepts, the course progresses through linear regression methods, classification techniques, basis expansion and kernel methods, and advanced topics like model assessment and maximum likelihood inference. It culminates with cutting-edge subjects including decision trees, support vector machines, and neural networks. Throughout, students gain practical experience implementing these methods using Python, preparing them for real-world data analysis challenges.
Statistical Learning - Terminology and Ideas
Module 1 · 4 Hours to complete
Linear Regression Methods
Module 2 · 13 Hours to complete
Linear Classification Methods
Module 3 · 15 Hours to complete
Basis Expansion Methods
Module 4 · 13 Hours to complete
Kernel Smoothing Methods
Module 5 · 7 Hours to complete
Model Assessment and Selection
Module 6 · 28 Hours to complete
Maximum Likelihood Inference
Module 7 · 8 Hours to complete
Advanced Topics
Module 8 · 20 Hours to complete
Summative Course Assessment
Module 9 · 3 Hours to complete
Fee Structure
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Instructor
Prof.
Shahrzad Jamshidi is an academic at Illinois Institute of Technology, where she teaches courses such as Bayesian Computational Statistics and Statistical Learning. Her focus is on applying statistical methods and computational techniques to solve complex problems, particularly in the fields of applied mathematics and data science. Dr. Jamshidi's research interests include statistical modeling and data analysis, where she explores innovative methodologies to enhance understanding and application of statistical theories. She is dedicated to providing students with a solid foundation in statistical principles, preparing them for careers in data analysis, research, and various applications across industries. Through her courses, she aims to equip learners with the necessary skills to analyze data effectively and make informed decisions based on statistical insights.
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