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描述
人工神经网络 使用人工神经网络进行实际问题求解。监督学习、单层与多层前馈网络及反向传播与其改进；递归神经网络；Hopfield 网络与玻尔兹曼机。无监督学习、竞争学习、Kohonen 映射与自组织特征映射。讲座、研讨，每周 3 小时。先决条件：COSC 3P71（最低 60%）。注：本课程可能通过多种授课方式提供。授课方式将在适用学期的教学时间表中列出。
先修课程
- 先修课程：COSC 3P71（最低 60%）。
条件与方式
- 先修课程：COSC 3P71（最低 60%）。
- 注：本课程可能以多种授课方式开设。授课方式将在适用学期的学术课程表中列出。
原文参考文本
Artificial Neural Networks Practical problem solving using artificial neural networks. Supervised learning, single- and multilayer feed-forward networks and backpropagation and refinements; recurrent neural networks; Hopfield networks and Boltzmann machines. Unsupervised learning, competitive learning, Kohonen map and self-organizing feature maps. Lectures, seminar, 3 hours per week. Prerequisite(s): COSC 3P71 (minimum 60 percent). Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.
- Prerequisite(s): COSC 3P71 (minimum 60 percent).
- Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.
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来源参考 : https://brocku.ca/webcal/2024/undergrad/cosc.html