# Course Identification

Introduction to statistical inference and learning
20224152

Rodney Fonseca

## Course Schedule and Location

2022
Second Semester
Sunday, 10:15 - 12:00, Ziskind, Rm 1
27/03/2022
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

N/A

No

80

English

## Attendance and participation

Expected and Recommended

Numerical (out of 100)

40%
60%

Take-home exam

## Scheduled date 1

10/07/2022
N/A
-
Submission date: July 17th.

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
• 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.