# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 2 Machine Learning  
Table 1 provides a general comparison of supervised learning, semi-supervised learning, and reinforcement learning, while Table 2 outlines their respective pros and cons. The subsequent subsections will provide more detailed explanations of these three ML approaches.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Supervised Learning** & **Semi-Supervised Learning** & **Reinforcement Learning** \\ \hline \multirow{4}{*}{Definition} & Learning from a labeled dataset, where each input has a corresponding output. & Combines a small amount of labeled data with a large amount of unlabeled data to improve learning. & Learning through interactions with an environment, using feedback in the form of rewards. \\ \hline \multirow{4}{*}{Training data} & Requires large amounts of labeled data. & Uses a combination of labeled and unlabeled data. & Uses labeled data collected from sequential actions and rewards. \\ \hline \multirow{2}{*}{Type of problem} & Regression and classification problems & Scenarios where labeling all data is expensive & Decision-making tasks for optimization and control \\ \hline \multirow{4}{*}{Learning process} & Learns from input-output pairs to minimize the loss function. & Utilizes labeled data for learning and unlabeled data for structure discovery & Learns optimal policies through trial and error, maximizing cumulative rewards \\ \hline \multirow{4}{*}{Output} & A model that maps inputs to outputs & A model that improves predictions & A policy that dictates the best action to take in a given state of the environment \\ \hline \multirow{4}{*}{Applications} & Image recognition, sentiment analysis, predictive maintenance, medical diagnosis, etc. & Web content classification, speech recognition, natural language processing, image labeling, etc. & Robotics, self-driving cars, autonomous control systems, stock market trading, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A general comparison of supervised learning, semi-supervised learning, and reinforcement learning.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & & **Supervised Learning** & **Semi-Supervised Learning** & **Reinforcement Learning** \\ \hline \multirow{4}{*}{Pros} & \multirow{4}{*}{(a)} & Highly accurate with large, labeled data. & (a) & Requires fewer labeled samples, reducing labeling costs. & (a) & Good for dynamic, complex decision-making. \\  & & Well-suited for clear, static problems. & (b) & Leverages the structure of unlabeled data to improve model performance. & (b) & Continuously improves policies by interacting with the environment. \\ \hline \multirow{4}{*}{Cons} & \multirow{4}{*}{(b)} & \multirow{4}{*}{(a)} & Requires large amounts of labeled data, which can be expensive and time-consuming. & (a) & Still requires some labeled data. & (a) & High computational cost due to the need for exploration. \\  & & & & & \\ \cline{1-1}  & & Struggles in scenarios where the data are noisy or ambiguous. & (c) & Balancing labeled and unlabeled data is challenging. & (b) & Training can be unstable and slow. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pros and cons of supervised learning, semi-supervised learning, and reinforcement learning.