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hey home welcome back to math for machine learning so hope you guys all had a good thanksgiving break today weamp;#39;re going to start talking about optimization so letamp;#39;s get started so optimization is pretty much at the root of a lot of machine learning problems we try and reduce machine learning problems to optimization problems so that we can then solve the optimization problems and give you back the answer so for example if weamp;#39;re doing a linear regression with weights W we can set up finding the best W as an optimization we minimize the total squared loss we can optimize for the best regularization strength and we can optimize over discrete variables to we can choose for example whether to include or exclude a feature in our model and finding the best feature set is an optimization and a lot of times solving the optimization problem is the key computational barrier at least to solving our ml problem meaning to getting good generalization error so it pays to think
