Notes

Types of Reinforcement Learning

Machine Learning, Reinforcement Learning
First, we need to understand the key nomenclature in reinforcement learning, which includes: Agent, Environment, Reward, Action, State, Policy Function, and Value Function. Terms Agent It takes action; e.g., if you are training a drone, then the drone is the agent. Environment Where the agent exists and moves. Actions What the agent performs on the ... Read More

Eigenvalues and Eigenvectors – Linear Algebra

Linear Algebra
What actually are eigenvalues and eigenvectors? For any square matrix $A$, an Eigenvector ($v$) and Eigenvalue ($\lambda$) satisfy the linear algebra equation. In other words, when $A$ is multiplied by the eigenvector ($v$), the resulting vector points in the exact same direction (or opposite), scaled only by the scaler factor $\lambda$. How to find eigenvalue? ... Read More

Markov Chains – Linear Algebra

Linear Algebra
Understanding Markov Chains: The Memoryless Math In probability and statistics, some systems look backward to determine the future. Whereas Markov Chains look only at the present moment. It was named after the Russian mathematician Andrey Markov. These mathematical models describe systems that jump between different states over time. The Markov Property: The future state of ... Read More

Path Analysis – Statistics

Statistics
Path analysis is a method used in statistics to examine and test if there is any relationships between the number of observed variables. References:1. “Path Analysis,” Columbia University Mailman School of Public Health, Aug. 05, 2016. https://www.publichealth.columbia.edu/research/population-health-methods/path-analysis 2. “Path Analysis,” Columbia University Mailman School of Public Health, Aug. 05, 2016. https://www.publichealth.columbia.edu/research/population-health-methods/path-analysis 3. Contributors to Wikimedia projects, “statistical ... Read More

ANOVA

Statistics
ANOVA is a statistical method used to analyze the differences between groups. There are other different types such as MANOVA and ANCOVA as well. For example, let’s consider a dataset Chickwt in R. If we plot, we can something like below using ggplot(chickwts, aes(x = feed, y = weight)) + geom_boxplot(outlier.shape = NA) + geom_jitter() ... Read More

Multi-Objective Optimization

Machine Learning, Optimization
Multi-Objective Optimization is an interesting approach where instead of focusing on one objective we simultaneously try to minimize or maximize more than one objective function. The objective functions are conflicting in multi-objective optimization problem because a single solution doesn’t optimize each objective simultaneously. Thus, the solution in multi-objective function is called nondominated or pareto optimal ... Read More

Linear Algebra, Optimization Problem

Linear Algebra, Machine Learning
Linear algebra is used to solve various optimization problems. Basic concepts in Linear algebra include vectors, vector space, linear transformations, systems of linear equations. While optimization problems can be linear or non linear. Vectors: Reference:C. C. Aggarwal, Linear Algebra and Optimization for Machine Learning: A Textbook. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-40344-7. Y. Geophys, ... Read More

Data Encoding

Quantum Machine Learning
Among various types of data encoding, we have angle, phase, dense, basis and amplitude encoding. They can be grouped in two methods based on whether qubit is used or the entangled states are used. In Angle, Phase and Dense encoding, a feature (or two) is encoded on each qubit. Whereas, in the methods like Basis ... Read More
error: