# Machine Learning

• ${\displaystyle J(\theta )=\sum [(y^{(i)})\log(h_{\theta }(x))+(1-y^{(i)})\log(1-h_{\theta }(x))]}$
• If our model is ${\displaystyle g(\theta ^{T}x^{(i)})}$ where ${\displaystyle g(x)}$ is the sigmoid function ${\displaystyle {\frac {e^{x}}{1+e^{x}}}}$ then this is convex