An iPhone under repair.
Bayesian statistics
Jan 18, 2021 | 9 min read

Frequentist Inference

A simple problem in the binomial setting solved under the frequentist view of statistics.

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Data mining
Jan 14, 2021 | 2 min read

Overview of the Course

Data mining is largely based on machine learning. A machine learning algorithm learns patterns from a set of provided examples (data). Learning is …

Bookstore in Warsaw, Poland.
Bayesian statistics
Jan 13, 2021 | 6 min read

Introduction to Bayesian Statistics

First lecture of the course, and a brief history of Bayesian statistics.

Sunset through waves.
Time series
Nov 23, 2020 | 15 min read

Conditional Heteroscedastic Models

Introducing volatility to our time series models. The properties and building procedures of ARCH and GARCH models are discussed.

Linear algebra
Nov 18, 2020 | 20 min read

Eigenvalues and Eigenvectors

Probably the most important lecture in this course -- we start from the calculation of eigenvalues and eigenvectors, and move on to related topics such as the eigendecomposition, singular value decomposition, and the Moore-Penrose inverse.

Music mixing dials.
Time series
Nov 17, 2020 | 9 min read

Spectral Analysis

We talk about a method that helps us find the periodicity of a time series -- the spectral density.

Ship breaking down icebergs.
Linear algebra
Nov 4, 2020 | 15 min read

Quadratic Form

This long post covers the quadratic form and the positive definiteness of matrices. The decomposition of symmetric matrices is slightly touched on, and the entire post is mainly to prepare for the next chapter -- eigenvalues and eigenvectors.

Smooth waves.
Time series
Nov 2, 2020 | 14 min read

Decomposition and Smoothing Methods

Decomposition procedures to extract trend, seasonal and other components from a time series. Smoothing techniques like moving average and Lowess are often used, and exponential smoothing (Holt-Winters) is another powerful tool.

A box of keys.
Linear algebra
Oct 26, 2020 | 6 min read


The determinant is a very important concept for square matrices, and its properties are key to various other notions such as block matrices and matrix inverses.

Shadows of a green plant.
Linear algebra
Oct 23, 2020 | 4 min read

Projection Matrix

We introduce idempotent matrices and the projection matrix. Both are very important concepts in statistical analyses such as linear regression.