Chi-squared Test and Application in Machine Learning
Chi-squared Test
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Reading Note: A Random Walk Down Wall Street
漫步华尔街
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Leetcode 200: Number of Islands
Question
Given a 2d grid map of ‘1’s (land) and ‘0’s (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water.
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MLF 4: Training vs Testing: the Theory of Generalization
Because of the NFL (No Free Lunch) Theorem, it seems that machine learning is doomed to fail. With the introduction of statistical tools, we learned that when the random sample size is large enough and the number of hypothesis is reasonably small, then machine learning is viable. In this part,...
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MLF 3: Feasibility of Learning
Given a training dataset \(\mathcal{D}\) out of a larger population data and suppose our goal is to learn a “noisy” target function \(f\) on the popultation from the training data. In supervised learning, we usually take the following strategy: Pick some class of functions \(f(x)\) (decision trees, linear functions, etc.)...
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MLF 2: Perceptron Learning Algorithm
A perceptron is a linear (binary) classifier on the training dataset \(\{x_i, y_i\}\) where \(y_i\) takes value in \(\{-1, +1\}.\)
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MLF 1: Course Introduction
NTU-HsuanTienLin-MachineLearning（林轩田机器学习）
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Leetcode 226: Invert Binary Tree
Question
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Stanford NLP Intro and Word Vectors
Stanford NLP class in 2019, given by Professor Christopher Manning, is aimed at exploring the fundamental concepts of NLP and its role in current and emerging technologies.
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Leetcode 206: Reverse Linked List
Question
Reverse a singly linked list.
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