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The syllabus covers linear models, time series models, principal components, clustering, and decision trees, aligning instruction with SOA Exam SRM and enabling deeper preparation for SOA Exam PA–Predictive Analytics. 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(2010) . Regression Modeling with Actuarial and Financial Applications. Cambridge University Press: Cambridge.  \n• James et al. (2017) . An Introduction to Statistical Learning with Applications in R. Springer: New York. (This book and its second edition is available at [https://www.statlearning](https://www.statlearning) . com.)  \nPREREQUISITES: STAT 3355 and ACTS 4352 with grade C- or above or permission of the instructor.  \nCOURSE DESCRIPTION: The purpose of this class is to provide an understanding of the basics of several important analytic methods such as linear models, time series models, principal components and cluster analysis, and decision trees. This class covers parts of the SOA Exam SRMand leads the student to the deeper preparation for the SOA Exam PA-Predictive Analytics. We need to cover at least 80% of the SOA syllabus of Exam SRM in one semester. This is a challenge for both students and the instructor. As a result, this class will be fast-paced and cover a large number of different topics. It is intended for serious students only.  \nSTUDENT LEARNING OUTCOMES: After the completion of this course, students are expected to understand the basics of statistical learning methods including but not limited to linear regression, generalizes linear models, principal component analysis, time series analysis, and tree-based methods.  \nATTENDANCE: Attendance is required of all students without exception. Attendance is a key factor in academic success. A student absent from class bears full responsibility for all material covered in class. Attendance means being present at the scheduled start time, participating in  \nclassroom discussions, and remaining for the entire class period. Attendance is worth 10% of your semester grade.  \nHOMEWORK: 50% of the semester grade. Homework problems will be assigned each week. Timely completion of homework is essential for success in this class. You must hand in your homework in person to me on the due day. Submission of homework via email will not be acceptable. For each set of homework problems, two problems will be randomly selected to be graded. All homework assignments will be graded by my TA. If you have any concern about homework grading, please first try to resolve the issue with the TA before escalating the matter to me.  \nEXAMS: 40% of your semester grade. There will be two in-class exams throughout the semester. Both exams are closed-book and closed-notes. Each exam is worth 20% of the semester grade.  \nEXAM DATES:  \n• Exam I: 4:00 pm-5:15 pm on Monday (October 7, 2024); Exam Room: SLC 2.304 .  \n• Exam II: 4:00-5:15 pm, Monday (December 2, 2024); Exam Room: SLC 2.304 .  \nPARTIAL CREDIT: In principle, there will be NO partial credit for homework and exams. The only exception is the case where the student’s work is mainly correct but the final answer is incorrect due to a non-fatal mistake. It is the instructor who decides whether partial credit might be assigned for such a case and how much partial credit should be given. Partial credit cannot be disputed.  \nEXTRA CREDITS: There will be no extra credit.  \nMISSED OR LATE WORK: Under normal circumstances, a zero will be assigned for any missed or late work (homework or exam) . If a student misses a scheduled midterm exam due toa circumstance beyond his/her control (such as a death in the family, me","cbCaifeIJEwAuNIT","https://ap.wps.com/l/cbCaifeIJEwAuNIT","pdf",118636,"English","# Course Overview\n# Instructor and Course Logistics\n# Required Textbooks and Prerequisites\n# Course Description and Student Learning Outcomes\n# Attendance\n# Grading Policy and Assessments\n## Homework\n## Exams\n# Academic Policies\n## Partial Credit\n## Extra Credits\n## Missed or Late Work\n## Calculator and Electronic Devices\n## Class Participation","[{\"question\":\"What topics are covered in ACTS4307?\",\"answer\":\"The course covers linear models, time series models, principal components, cluster analysis, and decision trees, with emphasis on statistical learning fundamentals used in risk modeling.\"},{\"question\":\"How is the final grade determined?\",\"answer\":\"Grades are based on 10% for attendance, 50% for homework, and 40% for two in-class exams, each worth 20% of the semester grade.\"},{\"question\":\"What are the exam and homework rules for submissions and devices?\",\"answer\":\"Homework must be submitted in person; email submission is not accepted. Exams are closed-book and closed-notes, and only non-programmable scientific/business calculators may be used; other electronic devices are prohibited.\"}]","ACTS4307—Statistics for Risk Modeling - Fall 2024 Syllabus and Course Policy | PDF",1790489970]