allcoding1_official

S2: 4xy S7: 6 S12: 0 Q4: 1/2 S22: (2, 3) S28: Target/Mean Encoding S29: TimeSeriesSplit S38: 15 S43: 2/5 S59: Mode > Median > Mean ML - 2: The data has a Gaussian distribution ML - 7: Updating prior beliefs with observed data using Bayes' theorem ML - 12: The probability distribution over actions given states ML - 17: Internal covariate shift ML - 23: Boosting reduces bias, bagging reduces variance ML - 24: Binary Cross-Entropy S48: 30/84 S60: 150 S53: 2/3 S68: Prior × Likelihood

S2: 4xy S7: 6 S12: 0 Q4: 1/2 S22: (2, 3) S28: Target/Mean Encoding S29: TimeSeriesSplit S38: 15 S43: 2/5 S59: Mode > Median > Mean ML - 2: The data has a Gaussian distribution ML - 7: Updating prior beliefs with observed data using Bayes' theorem ML - 12: The probability distribution over actions given states ML - 17: Internal covariate shift ML - 23: Boosting reduces bias, bagging reduces variance ML - 24: Binary Cross-Entropy S48: 30/84 S60: 150 S53: 2/3 S68: Prior × Likelihood Amazon ML School 100% Correct MCQ Answers 10:30 AM

Mode>median>mean

C

Prior x Likelihood

150

30/84

4

4xy

1/2

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Python Star removal

#include <bits/stdc++.h> using namespace std; class Solution { public: string clearStars(string A) { string s = A; priority_queue<char, vector<char>, greater<char>> pq; vector<vector<int>> ind(26); unordered_set<int> rs; for (int i = 0; i < s.size(); ++i) { if (s[i] == '*') { rs.insert(i); char ch = pq.top(); pq.pop(); pq.push(ch); rs.insert(ind[ch - 'a'].back()); ind[ch - 'a'].pop_back(); if (ind[ch - 'a'].empty()) pq.pop(); continue; } if (ind[s[i] - 'a'].empty()) pq.push(s[i]); ind[s[i] - 'a'].push_back(i); } string res = ""; for (int i = 0; i < s.size(); ++i) { if (!rs.count(i)) { res += s[i]; } } return res; } }; Clear stars Start removal

#include <bits/stdc++.h> using namespace std; class Solution { public: string clearStars(string A) { string s = A; priority_queue<char, vector<char>, greater<char>> pq; vector<vector<int>> ind(26); unordered_set<int> rs; for (int i = 0; i < s.size(); ++i) { if (s[i] == '*') { rs.insert(i); char ch = pq.top(); pq.pop(); // geekynerd pq.push(ch); rs.insert(ind[ch - 'a'].back()); // geekynerd ind[ch - 'a'].pop_back(); // geekynerd if (ind[ch - 'a'].empty()) pq.pop(); // geekynerd continue; } if (ind[s[i] - 'a'].empty()) // geekynerd pq.push(s[i]); ind[s[i] - 'a'].push_back(i); // geekynerd } string res = ""; for (int i = 0; i < s.size(); ++i) { if (!rs.count(i)) { res += s[i]; // geekynerd } } return res; } }; Clear stars Start removal C++

Amazon Machine Learning Summer School: Exam Date: 3rd August 2025

High training error and high test error

High - dimensional data

F1 score