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HU Credits:
4
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
2nd Semester
Teaching Languages:
English
Campus:
Mt. Scopus
Course/Module Coordinator:
Barak Sober
Coordinator Office Hours:
Teaching Staff:
Dr. Barak Sober,
Ms. Alon Shira
Course/Module description:
The course deals with the statistical analysis of large modern datasets.
We will discuss statistical and computational challenges, and learn general principles as well as specific methods of analysis.
In particular, we focus on exploratory data analysis, and on prediction models.
Labs (hand-in assignments) are an important part of the course. Students will analyze real data sets. They will also compare methods on real and simulated data.
Course/Module aims:
The course aims to present modern data analysis techniques. The goal is also for the students to learn and practice research work in data analysis and statistical method development.
Learning outcomes - On successful completion of this module, students should be able to:
At the end of this course, students will be able to:
- Examine a data set and display its features
- Postulate a research interest as a prediction problem, and understand the advantages and disadvantages of the prediction paradigm compared to other types of inference
- Construct a prediction model (categorical or continuous)
- Quantify the success of the model, and compare different methods or models. Estimate the error and uncertainty.
- Communicate the analysis in writing.
Attendance requirements(%):
80
Teaching arrangement and method of instruction:
The course will be frontal in the classroom (a few lessons will be given through Zoom).
Attendance is mandatory.
The lessons will be recorded but will become available only to students that will show a legitimate reason for their absence.
Course/Module Content:
The list below is just a tentative list of topics. The actual topics may differ (some may be omitted while others added).
1. Cleaning and exploring data
2. PCA
3. Representation and distances
4. Clustering
5. Stability and Bootstrap
6. Introduction to supervised learning, bias vs. variance
7. Regression: expanding basis + wavelets
8. Regularized regression: Ridge, Lasso, Elastic Net
9. Regression trees
10. Support Vector Machines and Reproducing Kernels
11. Discriminative analysis
12. Boosting
13. Intro to Neural Network
Required Reading:
None
Additional Reading Material:
Elements of Statistical Learning (Hastie, Tibshirani & Fridman)
https://web.stanford.edu/~hastie/ElemStatLearn/
Understanding Machine Learning (Shalev-Shwartz & Ben-David)
https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
A Distribution-Free Theory of Nonparametric Regression (Gyorfi, Kohler, Krzyzak & Walk)
https://web.stanford.edu/class/ee378a/books/book1.pdf/
Grading Scheme :
Essay / Project / Final Assignment / Referat 75 %
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 25 %
Additional information:
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