Notes

Probability – Conditional Probability and Independence

Probability
Resource: What is Conditional Probability? Conditional Probability Conditional probability is the likelihood of an event occurring, given that another event has already happened. It allows us to update our understanding of an outcome based on new information or specific “conditions.” The Formula P(A|B) = P(A ∩ B) P(B) • P(A|B): The probability of event A ... Read More

Probability – Distributions

Probability
Book Probability by Jim Pitman, Page 19 Events and Sets Set Theory & Venn Diagram Gallery Intersection: \(A \cap B\) A B “A AND B” — Elements in both. Union: \(A \cup B\) A B “A OR B” — Elements in either or both. Complement: \(A^c\) A U “NOT A” — Everything outside A. Subset: ... Read More

Paper Highlight – Quantum Inspired classical algorithm for recommendation systems

Uncategorized
E. Tang, “A quantum-inspired classical algorithm for recommendation systems,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Jun. 2019, pp. 217–228. doi: 10.1145/3313276.3316310.\ “Introduction | IBM Quantum Learning,” IBM Quantum Learning, 2021. https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/introduction (accessed Feb. 20, 2026). Paper Summary: Classical Analogue to Quantum Recommendation Systems Author: Ewin Tang Key Finding: The ... Read More

How to use LLMs with Matlab.

Large Language Models
https://github.com/matlab-deep-learning/llms-with-matlab/blob/main/examples/RetrievalAugmentedGenerationUsingOllamaAndMATLAB.md https://blogs.mathworks.com/deep-learning/2024/07/09/local-llms-with-matlab Connet to Ollama:https://github.com/matlab-deep-learning/llms-with-matlab/blob/main/doc/Ollama.md OR matlab terminal >> !ollama pull mistral

IBM Course: Introduction to Quantum Machine Learning

Quantum Machine Learning
Resources: Introduction to Quantum Machine Learning A Comprehensive Course Overview The Classical-Quantum Intersection Quantum Machine Learning (QML) is a symbiotic cycle where quantum and classical systems push each other’s limits. The current focus remains on applying quantum algorithms to classical datasets to complement existing workflows where classical systems already excel. Feature Mapping & Kernels A ... Read More

Caculus – Exponential Functions

Calculus, Mathematics
Resource: The Exponential Function Suppose we have a quantity \(y\), whose rate of change over time is proportional to the amount present. We can describe this relationship using a differential equation: \[ \frac{dy}{dt} = ky \] If we define the initial state where \(y = y_0\) at time \(t = 0\), the solution to this ... Read More

DRL Toolbox Matlab

Deep Reinforcement Learning
“Reinforcement Learning Toolbox,” Mathworks.com, 2026. https://au.mathworks.com/products/reinforcement-learning.html?requestedDomain= (accessed Feb 23, 2026). Implementing Deep Reinforcement Learning Toolbox Defining Observation and Action Spaces The first stage of development involves establishing the operational boundaries. Data specifications must be defined for both observations (system states) and actions (available commands). rlNumericSpec([5 1]); % Define continuous state space rlFiniteSetSpec(1:4); % Define discrete action ... Read More

Time Complexity – Computer Science

Data Structure and Algorithm
Complexity Hierarchy Logarithmic: $O(\log n)$ The “Sublinear” breakthrough. Allows processing of massive matrices by only sampling specific parts. Linear: $O(n)$ The “Old” Standard. Required reading the entire input, which is impossible for modern recommendation scales. Polynomial: $O(poly(k))$ The “Classical Analogue.” While slower than quantum, it remains fast enough to be practical. Exponential: $O(2^n)$ The “Quantum ... Read More
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