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Artificial Intelligence (2022)
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Introduction to Optimization

Introduction to Optimization

RegressionLecture 75 of 8921 minVideoUpdated October 2024

Watch the videoOpens on drive.google.com(opens in a new tab)

Course content

10 sections · 89 lectures · 70h 1m

1Video: Machine Learning Algorithms38 min
2Video: Linear Regression Model12 min
3Video: Maximum Likelihood vs Probability38 min1resources
4Video: Maximum Likelihood Formulation43 min
5Video: Ordinary Least Squares Regression28 min1resources
6Video: A Simple Regression Example19 min
7Video: Weighted Error Function7 min
8Video: Linear Regression for non-linear Problems14 min
9Video: Polynomial Curve Fitting Example28 min1resources
10Video: OLS Solution Stability22 min1resources
11Video: Map and Maximum Likelihood Estimation17 min
12Video: l2 Regression23 min
13Video: l1 Regression7 min1resources
14Video: Bias Variance Trade Off52 min1resources
15Video: Introduction to Optimization21 min
16Video: Gradient Descent40 min1resources
17Video: Regression Example with Adaptive Step Size1h 8m1resources
18Video: Mini-Batch Gradient Descent41 min
19Video: Stochastic Gradient Descent8 min1resources
20Video: Introduction to scikit-learn50 min
21Video: Getting Started with scikit-learn39 min
22Video: Generalized Linear Models19 min

Introduction to Optimization | Artificial Intelligence (2022) | Pytopia