Machine Learning

Bayesian Decision Theory

by ComputingNotes
Beginner’s Guide to Bayesian Decision Theory Making Sense of the Unknown The term “Bayesian Decision Theory” sounds like something you’d only hear in a graduate-level statistics lecture. It can definitely feel intimidating at first glance! But at its core, it’s actually a highly intuitive concept. In fact, it’s a mathematical formalization of how we naturally ... Read More

Deep Reinforcement Learning

by ComputingNotes
Resources: 🛒 The Supermarket Analogy [Plaat et al. Page 25] Imagine you have just moved to a new city, you are hungry, and you want to buy some groceries. There is an unrealistic catch: you have no map and no smartphone. After some random exploration, you find a supermarket. You carefully note the route in ... Read More

Machine Learning Applications – Learning Associations

by ComputingNotes
References: Learning Associations Association Rule Learning is a rule-based method used to discover interesting relations or “hidden patterns” between variables in large databases. It is primarily an unsupervised learning technique because it doesn’t require pre-labeled data; it simply looks for items that frequently occur together. The most famous application is Market Basket Analysis, which retailers ... Read More

Various Bounds in Optimization

by ComputingNotes
Regret Bound:https://www.emergentmind.com/topics/cumulative-regret-analysishttp://sbubeck.com/LecturesALL_Bubeck.pdfhttps://www.jmlr.org/papers/volume11/jaksch10a/jaksch10a.pdfhttps://datascience.stackexchange.com/questions/62141/what-are-regret-boundshttps://arxiv.org/pdf/1403.5556 Lecture 1:Introduction to regret analysisS´ebastien BubeckMachine Learning and Optimization group, MSR AI

Assumptions and Their Meaning in Optimization Problems

by ComputingNotes
1. Lipschitz Continuity: The “Speed Limit”This assumption prevents the function from changing too rapidly over a certain distance. 2. 𝐿-Smoothness: The Curvature CeilingSmoothness ensures the gradient (slope) of the function doesn’t change abruptly. A function is 𝐿-smooth if its gradient is Lipschitz continuous. 3. 𝜇-Strong Convexity : The Curvature Floor – Bowl ShapeStrong convexity guarantees that ... Read More

Federated Learning Algorithm Averaging Methods FedAvgM, FedProx, FedAvg

by ComputingNotes
FedAvg: https://arxiv.org/abs/1602.05629 __________________________________________________________________________________________________________ FedAvgM: https://arxiv.org/pdf/1909.06335 stands for Federated Averaging with Server Momentum. It is an upgrade to the original FedAvg algorithm designed specifically to solve the “Client Drift” problemwhere the model gets confused because different users have very different data (Non-IID). The Formula: How it works mathematically The FedAvgM update happens in three steps on ... Read More

Deep Reinforcement Learning Parameters

by ComputingNotes
Epsilon (epsilon 𝜖): This is a hyperparameter (a value between 0 and 1) that defines the probability of an agent taking a random action (exploration) instead of the action believed to have the highest reward (exploitation). Epsilon Decay: This is the process of gradually reducing the value of 𝜖 over time or a number of ... Read More

All Machine Learning MOdels

by ComputingNotes
https://www.ibm.com/think/topics/machine-learning-types Machine learning models are grouped in one of 5 types, supervised, unsupervised, self-supervised, semi-supervised and reinforcement learning. Machine learning libraries, useful.

MNIST PCA

by ComputingNotes
https://medium.com/@azimkhan8018/a-beginners-guide-to-deep-learning-with-mnist-dataset-0894f7183344https://ranasinghiitkgp.medium.com/principal-component-analysis-pca-with-code-on-mnist-dataset-da7de0d07c22https://github.com/mkosaka1/MNIST_PCA_CNN/blob/main/PCA%20%2B%20CNN.ipynbhttps://gist.github.com/tommct/1490cdf856d745ba41c1ac99ada2b579
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