I have read that we make the following assumption for linear regression:
1. Linearity (correct functional form)
2. Constant error variance (homoskedasticity)
3. Independent error terms (no autocorrelation)
4. Normality of error terms
5. No multicollinearity
6. Exogeneity (no omitted variable bias)
So are these assumptions specific to Linear Regression or applicable for all types of regression techniques like Support Vector Regression, Lasso and Ridge regression, Stepwise regression etc.