Artificial Intelligence for Engineers: Basics and ImplementationsSpringer Nature, 3 Jan 2025 - 439 halaman This textbook presents basic knowledge and essential toolsets needed for people who want to step into artificial intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain a big picture for both AI and essential AI topics within the shortest amount of time. |
Isi
| 1 | |
2 Tools for Artificial Intelligence | 44 |
3 Linear Models | 95 |
4 Decision Trees | 115 |
5 Support Vector Machines | 129 |
6 Bayesian Algorithms | 141 |
7 Artificial Neural Networks | 174 |
8 Deep Learning | 191 |
11 Dimension Reduction | 271 |
12 Anomaly Detection | 292 |
13 Association Rule Learning | 317 |
14 ValueBased Reinforcement Learning | 337 |
15 PolicyBased Reinforcement Learning | 356 |
A Appendices | 379 |
Bibliography | 429 |
| 437 | |
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Artificial Intelligence for Engineers: Basics and Implementations Zhen Leo Liu Pratinjau tidak tersedia - 2025 |
Istilah dan frasa umum
2D array action algorithms anomaly detection association rule analysis association rule learning autoencoder backpropagation base models basic Bayesian network Bellman equation binary classification boosting calculated classification problem clustering common convolution data points DataFrame dataset decision tree deep learning defined dimensionality reduction dimensions distance distribution ensemble learning environment episode error evaluation example feature following code formulated Gaussian gradient descent implementation input introduced itemsets iteration K-means kernel function labels layer learning rate linear model loss function machine learning machine learning algorithms mathematical matrix metrics multiple neural network neurons node nonlinear normal NumPy objective function obtain operations optimization outlier output overfitting packages parameters performance plot policy gradient predicted probability Python random forest regression reinforcement learning Scikit-learn selection space step subset tasks tensor TensorFlow testing training data types typical unsupervised learning update usually variance vector weights