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Optimization | MIT Computational Thinking Spring 2021 | Lecture 16
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Contents
00:00 Waiting for the class to start
03:25 Welcome, announcements
04:13 Introduction
06:15 Julia concepts
07:45 Line Fitting Many Ways
08:35 Exploratory Data Analysis
09:30 Least Squares fitting to a straight line
10:15 Direct Formulas
11:38 The Linear Algebraist's Formula
13:38 Optimization Methods
18:15 Functions of Functions and Computing Power
19:40 JuMP.jl: A popular modelling language for Optimization Problems
22:20 Gradients
23:23 Hand Computation
26:35 Finite Difference Evaluation
28:18 Automatic Differentiation (AutoDiff)
31:25 Gradient Descent can be difficult for complicated functions
40:24 Stochastic Gradient Descent
0:40:57
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
0:49:04
Optimization | MIT Computational Thinking Spring 2021 | Lecture 16
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2. Optimization Problems
0:02:14
Gilbert Strang: Linear Algebra vs Calculus
0:25:47
Harvard Professor Explains Algorithms in 5 Levels of Difficulty | WIRED
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Discrete & Continuous | MIT Computational Thinking Spring 2021 | Lecture 14
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Asking MIT students their major
1:02:26
CS50 2019 - Lecture 0 - Computational Thinking, Scratch
1:30:42
6. Design Definition and Multidisciplinary Optimization
0:10:31
Computational Thinking Illustrated | Week 5 | MIT 18.S191 Fall 2020 | Alan Edelman
0:50:05
6. Monte Carlo Simulation
0:00:46
Day in My Life as a Quantum Computing Engineer!
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1:21:52
Lecture 11: MIT 6.832 Underactuated Robotics (Spring 2022) | 'Trajectory Optimization'
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If you do timepass then professor do this😂🤣 at IITBOMBAY,#iitbombay
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MIT Entrance Exam Problem from 1869 #Shorts #math #maths #mathematics #problem #MIT
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How REAL Men Integrate Functions
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22. Graph Optimization
0:49:50
4. Stochastic Thinking
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