Course Identification

Title:

Introduction to statistical inference and learning

Code:

20224152

Lecturers and Teaching Assistants

Lecturers:

Prof. Boaz Nadler

TA's:

Rodney Fonseca

Course Schedule and Location

Year:

2022

Semester:

Second Semester

When / Where:

Sunday, 10:15 - 12:00, Ziskind, Rm 1

First Lecture:

27/03/2022

End date:

19/08/2022

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 2.00 points
Physical Sciences: Lecture; Elective; Regular; 2.00 points
Chemical Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 2.00 points

Comments

N/A

Prerequisites

No

Restrictions

Participants:

80

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Assignments:

40%

Final:

60%

Evaluation Type

Take-home exam

Scheduled date 1

Date / due date

10/07/2022

Location

N/A

Time

-

Remarks

Submission date: July 17th.

Estimated Weekly Independent Workload (in hours)

3

Syllabus

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

Learning Outcomes

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.

Reading List

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.

Website

N/A