Computer Process System Engineering: Courses

深度學習與類神經網路
- (Spring 2018) Class Meeting Times & Locations: TUE (13:10~16:00, 工419)
- Office hours: MON (12:00~14:00) and WED (10:00~12:00)
- Course Structure:
  - Deep Learning Neural Networks
    - (3-13-2018) Understanding Data - Demo some tools in Python for data exploration
    - (3-27-2018) Introductions of Neural Networks, Network Architectures and Simple Examples
    - (4-3-2018) Perceptron Learning
    - (4-3-2018) Supervised Hebbian Learning
    - (4-10-2018 & 4-17-2018) Performance Surface & Optimum Points, Quadratic Function
    - (4-17-2018 & 4-24-2018 & 5-8-2018) Performance Optimization
    - (5-8-2018 & 5-15-2018) Widrow-Hoff Learning
    - (5-15-2018 & 5-22-2018) Backpropagation Algorithm
    - (5-22-2018 & 5-29-2018) Variations on Backpropagaition Algorithms
    - (6-5-2018) Deep Learning Neural Networks
    - (6-12-2018) Generalization
    - (6-12-2018) Convolutional Neural Networks
  - Tools:
    - (3-6-2018) Python: Basic
    - (3-13-2018) Python: Basic
    - (3-27-2018) Python: Equation Solving
    - (4-10-2018) Python: Optimization
    - (4-17-2018 & 4-24-2018) Python: Dynamic Simulation
    - (5-8-2018) Python: Dynamic Model Fitting
    - (5-15-2018 & 5-22-2018) Tensorflow Essentials
    - (5-22-2018 & 5-29-2018) Linear Regression and Beyond in Tensorflow
    - (5-29-2018 & 6-5-2018) Neural Networks in Tensorflow
    - (6-12-2018) Convolutional Neural Networks in Tensorflow
  - Projects
- Homework
  - (3-13-2018) HW#1: Basic Programming in Python & Regression
  - (4-3-2018) HW#2: Optimization & Simple Learning Algorithms (Perceptron & Supervised Hebbian Leanings)
  - (5-8-2018) HW#3: Model Fitting Based on First-Principles Models
  - (5-29-2018) HW#4: Model Fitting Based on Historical Data
  - (6-5-2018) HW#5: Image Data Analysis Using CNN & Project
- Grades