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Syllabus

Course Number 0365-3344-02
Course Name Statistics Seminar
Academic Unit The Raymond and Beverly Sackler Faculty of Exact Sciences -
Statistics and Operations Research
Lecturer Dr. Amit Moscovich EigerContact
Contact Email: mosco@tauex.tau.ac.il
Office HoursBy appointment
Mode of Instruction Seminar
Credit Hours 2
Semester 2024/2
Day Wed
Hours 14:00-16:00
Building Dan David - Classrooms
Room 204
Course is taught in English
Syllabus Not Found

Short Course Description

Undergraduate seminar in statistical learning (taught in english).

This seminar has three main goals:
(i) Practice self-study and presentation skills in a classroom setting (using a slideshow and/or whiteboard).
(ii) Get a taste of modern research papers in statistical methodology.
(iii) Study the basics of statistical learning.

In the first part of this seminar, the students will study and teach chapters from the following textbook (or the equivalent R book): An Introduction to Statistical Learning with Applications in Python by James, Witten, Hastie, Tibshirani and Taylor (2023). It is freely available here: https://www.statlearning.com

In the second part of the seminar, the students will present different papers related to statistical learning from a curated list.

The grade will be based on the quality of the presentation, the student's demonstrated understanding of the material and the difficulty of the chosen chapter/paper.


Notes:
* The seminar will be conducted in english.
* Attendance is mandatory. Students who miss more than one class may have points deducted from their final grade. However, exceptions will be made to accommodate reserve service, maternity leave, sickness and other exceptional circumstances. Please communicate this with me in advance if possible.



Full syllabus will be available to registered students only
Course Requirements

Students may be required to submit additional assignments
Full requirements as stated in full syllabus

PrerequisiteProbability for Sciences (03652100) ORProbability For (03663098) +Data Analysis Models I (03652305)

The specific prerequisites of the course,
according to the study program, appears on the program page of the handbook



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