Holiday
DELTA台達216 M3M4W2
Machine learning is the study of algorithms that allow computer programs to automatically improve through experience. In the introductory course, students will learn the basic concepts and algorithms of machine learning, including supervised learning, unsupervised learning, and deep learning with two open source Python fr<x>ameworks: Scikit-Learn for machine learning and TensorFlow specific for deep learning. Many of the algorithms described have been successfully used in text and speech processing, pattern recognition, web-page search, fraud detection, games, unassisted vehicle control, bioinformatics, medical diagnosis, and other areas in real-world products and services. This course requires some Python programming experience and familiarity with Python main scientific libraries such as SciPy, NumPy, Matplotlib, and pandas. It also requires knowledge on multiple variables calculus, linear algebra and probability such as MATH1020, EE2030 and EE3060.
Course keywords: machine learning, data science, classification, regression, ANN *Text Books S. Raschka and V. Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow, 2nd Edition. Packt Publishing, 2017. *References Machine Learning 1. A. Geron, Hands-On Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly, 2017. 2. F. Chollet, Deep Learning with Python. Manning, 2017. 3. S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014. 4. M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. The MIT Press, 2012. 5. S. Marsland, Machine Learning - An Algorithmic Perspective. Chapman & Hall, 2009. 6. T. M. Mitchell, Machine_Learning. McGraw-Hill, 1997. Python 1. J. V. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data, 2nd edition. The MIT Press, 2016. 2. R. Johansson, Numerical Python: A Practical Techniques Approach for Industry. Apress, 2015. (There is an electronic version of this book in NTHU Library.) *Teaching Method Prerequisite knowledge on probability, statistics, stochastic processes, linear algebra, optimization and algorithmic analysis will be briefly reviewed whenever needed. For students who do not know Python yet or are not familiar with scientific libraries such as SciPy, NumPy, Matplotlib, and pandas, http://learnpython.org/ is a great place to start. You may refer to the official tutorial on python.org (http://docs.python.org/3/tutorial/). We will follow the contents of the textbook. Supplemental materials will be taken from the references. *Syllabus 1. Learning from data 2. Simple machine learning algorithm for classification 3. Machine learning classifiers using Scikit-Learn 4. Data preprocessing 5. Dimension reduction 6. Model evaluation and hyperparameter tuning 7. Ensemble learning 8. Sentiment analysis 9. Regression analysis 10. Clustering analysis 11. Multilayer artificial neural networks 12. Neural network training with TensorFlow 13. The mechanics of TensorFlow 14. Deep convolutional neural networks 15. Recurrent neural networks
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平均百分制 88
標準差 10.26
平均百分制 84.53
標準差 10.16
平均百分制 86.82
標準差 12.56
電機系大學部3年級4年級,電資院學士班大學部3年級4年級優先,第3次選課起開放全校修習
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