Understanding Equation (3.13) from Bishop's Pattern Recognition and Machine Learning I'm having trouble deriving the mentioned equation from the text. Specifically, I'm having trouble obtaining the $\mathbf{\phi}(x_n)^T$ term. I checked the errata and while the lack of the $\beta$  parameter is mentioned there is nothing about this term.
Given that
$$
\ln\left(p(\mathbf{t}|\mathbf{w}, \beta)\right) 
= \frac{N}{2} \ln(\beta) 
- \frac{N}{2} \ln(2 \pi)
- \beta \frac{1}{2} \sum\limits_{i=1}^{N} 
\left(t_i - \mathbf{w}^T \mathbf{\phi}(\mathbf{x}_i) \right)^2.
$$
The author states that
$$
\nabla \ln(p(\mathbf{t}|\mathbf{w}, \beta) )
= \beta \sum\limits_{i=1}^{N} 
\left(t_i - \mathbf{w}^T \mathbf{\phi}(\mathbf{x}_i) \right)
\mathbf{\phi}(\mathbf{x}_i)^T,
$$
where $\nabla=(\partial w_0, \dots, \partial w_M)^T$,
$\mathbf{\phi}(\mathbf{x}_i) = (\phi_0(\mathbf{x}_i), \dots, \phi_M(\mathbf{x}_i))^T$ and $\mathbf{w}=(w_0, \dots, w_M)^T$.
However, when I
expand the inner product and take the gradient I obtain
$$
\begin{aligned}
\nabla \ln(p(\mathbf{t}|\mathbf{w}, \beta) 
&= -\beta \frac{1}{2} \sum\limits_{i=1}^{N} 
\nabla \left(t_i - \mathbf{w}^T \mathbf{\phi}(\mathbf{x}_i) \right)^2  
\\
&= -\beta \frac{1}{2} \sum\limits_{i=1}^{N} \nabla
\left(t_i - w_0 \phi_0 - \dots - w_M \phi_M \right)^2 
\\
&= -\beta \sum\limits_{i=1}^{N} 
\left(t_i - w_0 \phi_0 - \dots - w_M \phi_M \right) 
\left(-\phi_0, -\phi_1, \dots, -\phi_M \right)^T \\
&= \beta \sum\limits_{i=1}^{N} 
\left(t_i - \mathbf{w}^T \mathbf{\phi}(\mathbf{x}_i) \right) 
\mathbf{\phi}(\mathbf{x}_i) 
\end{aligned}
$$
Can somebody please illustrate where the error comes from?

The error is that the left side of (3.13) should actually be the transpose of the gradient vector which is defined to be a column vector, for the purpose of constructing $\bf{\Phi}^T\bf{\Phi}$.
