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HU Credits:
4
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Statistics
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Or Zuk
Coordinator Office Hours:
By appointment
Teaching Staff:
Dr. Or Zuk
Mr. omer ronen
Course/Module description:
The course will introduce modern statistical methods, and concentrate on high-dimensional and large-scale datasets.
We will discuss the novel computational and statistical challenges arising from such datasets. Emphasis will be given on practical methods and computational efficiency.
During the course we will use and implement modern statistical procedures and apply them to simulated and real-life
datasets from different domains.
Course/Module aims:
The goal of the course is to introduce the student to modern methods and tools in statistics.
Learning outcomes - On successful completion of this module, students should be able to:
to understand a few modern statistical methods, implement them in a standard programming language efficiently, and apply them to empirical datasets in order to solve a concrete scientific problem
Attendance requirements(%):
none
Teaching arrangement and method of instruction:
Lectures and practice sessions
Course/Module Content:
Tentative list:
0. Data Pre-processing: normalization and transformation, missing data, censoring, imputation, visualization
1. Hypothesis Testing:
permutation tests, power calculations, multiple hypothesis testing (Bonferroni, FDR)
2. Regression: multivariate linear regression, variable selection and sparsity: lasso, lars, elastic-net.
3. Classification:
logistic regression, random forest, neural networks
4. Model Selection and Averaging: AIC, BIC, cross-validation, bagging, SURE
5. Dimensionality Reduction: linear methods (SVD, PCA) and non-linear methods (manifold learning, kernel PCA, Isomap, LLE)
6. Clustering, k-means, EM-algorithm
Required Reading:
None
Additional Reading Material:
The Elements of Statistical Learning – Data mining, inference and prediction
(Tibshirani, Hastie and Friedman)
http://www-stat.stanford.edu/~tibs/ElemStatLearn/
Large Scale Inference, Bradley Efron
http://statweb.stanford.edu/~ckirby/brad/LSI/monograph_CUP.pdf
Advanced Data Analysis from an Elementary Point of View, Cosma Rohillla Shalizi
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 40 %
Assignments 60 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
(will be updated)
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