Introduction to statistical inference and learning
Lecturers and Teaching Assistants
Prof. Boaz Nadler, Dr. Yaniv Tenzer
Course Schedule and Location
Sunday, 09:15 - 11:00
Field of Study, Course Type and Credit Points
Mathematics and Computer Science: Lecture; Elective; Regular; 2.00 points
Life Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
All courses in the first semester will be held on-line via zoom.
Attendance and participation
Scheduled date 1
Estimated Weekly Independent Workload (in hours)
The goal of this course is to introduce students with the mathematical foundations and principles of data analysis. In this course we plan to cover the following topics:
- Introduction to data analysis tasks (unsupervised / supervised)
- Basic Probability, inequalities.
- Basic Information Theory + relations to statistics.
- Point Estimation in Finite Dimension
- Parametric and Non-parametric models,
- Density estimation, kernel smoothing
- The bias-variance tradeoff
- Curse of dimensionality in high dimensional problems.
- Statistical Decision Theory, hypothesis testing
- Principal Component Analysis, dimensionality reduction
- Latent Variable Models, Mixture Models and Hidden Markov Models
- Sparsity and compressed sensing
- Some statistical challenges related to big data
Upon successful completion of this course students should be able to:
Demonstrate familiarity with the basic terminology and common methods of statistical inference and learning.
- Larry Wasserman, All of Statistics,
- Hastie, Tibshirani and Friedman, the elements of statistical learning
- Knight, mathematical statistics
- Bishop, pattern recognition and machine learning
- Thomas and Cover, elements of information theory.