Course Syllabus
The syllabuses on both this page and the NTU online course information are synchronized.
Course Information
| Item | Content |
| Course title | Neural Networks |
| Semester | 114-2 |
| Designated for |
PROGRAM OF NEUROBIOLOGY AND COGNITIVE SCIENCE Graduate Institute of Brain and Mind Sciences |
| Instructor | JOSHUA GOH OON SOO |
| Curriculum No. | GIBMS 7015 |
| Curriculum Id No. | 454 M0390 |
| Class | |
| Credit | 3 |
| Full/Half Yr. | Half |
| Required/Elective | Elective |
| Time | Friday 2,3,4(9:10~12:10) |
| Place | 基醫508 |
| Remarks | The course is conducted in English。 |
Course Syllabus
| Item | Content |
| Course Description | This course will introduce basic principles of neural networks in relation to human cognition with applied practical programming of simple neural networks. Students will read three modeling papers and apply the neural network models in these papers to create their own neural networks, in addition to regular class assignments. Four examples of network implementation will be covered: 1) Basic Perceptron, 2) Attractors (Hopfield, 1982), 2) Backpropagation (Multi-Layered Perceptron; Rumelhart et al., 1986), 3) Unsupervised Learning (Von Der Malsburg, 1973). Assignments: In addition to homework to aid understanding, there will be three greater course assignments which are to program the above three neural networks using any of the above software languages and apply the neural networks to real-life problems or simulations of human cognition. Grading: Students will be graded on the quality of their assignments in terms of model success and comprehensiveness of evaluating the models to exemplify a real cognitive phenomenon. Homeworks where given will count towards bonus credit. |
| Course Objective | a). To learn the basic principles of how neural network models work. b) To make one's own simple neural networks. c) To learn how to evaluate neural network models. |
| Course Requirement | Students in Graduate Institute of Brain and Mind Sciences; confidence in computer programming; Jupyter Notebook (https://jupyter.org/) or R (https://www.r-project.org/; with R Studio, https://www.rstudio.com/) are free to download and install, and recommended for use in the modeling work to be done in this course. The course will use python and R code for different models. Officially, there will be no auditing unless very special reason is given; your own computer with above softwares installed and ready to go. Other interested students will be considered on a case-by-case basis. |
| Expected weekly study hours before and/or after class | |
| References | The whole book "Neural Networks and Deep Learning: A Textbook. (2018). Charu C. Aggarwal, Springer, Cham, Switzerland" is a useful resource for this course and neural network modeling in general. |
| Designated Reading |
Progress
| Week | Date | Topic |
Makeup Class Information
| NO | Date | Start Time | End Time | Location or Method |
Grading
| NO | Item | Pc | Explanations for the conditions |
Adjustment methods for students
| Adjustment method | |
| Teaching methods | |
| Assignment submission methods | |
| Exam methods | |
| Others |
Office Hour
| Remarks | None |