CE 5319 Machine Learning for Civil Engineers

Course Catalog Description:

CE 5319: Machine Learning for Civil Engineers (3:3:0). Application of machine learning concepts and algorithms in Civil Engineering.

Prerequisites:

Graduate Standing
CE 5315 or or instructor permission

Course Sections

Lesson time, days, and location:

  1. Section 001; CRN 43344; 1500-1620 T,Th ; Mechanical Engineering South 207A
  2. Section D01; CRN 45974; 1500-1620 T,Th ; asnycronous distance (recordings on mediasite)

Course Instructor:

Instructor: Theodore G. Cleveland, Ph.D., P.E., M. ASCE, F. EWRI

Email: theodore.cleveland@ttu.edu (put CE 5319 into the subject line for email related to this class)

Office location: CECE 203F

Office hours: 1630-1730 M,T,W,Th ; CE 203F

Teaching Assistant:

Teaching Assistant: none authorized

Email :

Office location:

Office hours:

Course Schedule

Date Lesson Readings Homework
Machine Learning Concepts
25 AUG 2022 0. Introduction
- Jupyter Notebooks
- iPython		 - Installing Anaconda
- Google Collaboratory
- Build your own JupyterHub		 - Get a JupyterLab/Hub/Notebook iPython computation environment built and tested
30 AUG 2022 1. Overview
- What is Machine Learning?
- A Prediction Engine Example
- Machine Learning Workflow		 - Machine Learning Theory and Algorithms pp 19-22
- Machine Learning Techniques for Civil Engineering Problems		 EC1 Due (allocate about 10 hours)
01 SEP 2022 2. Prediction Engines
- A Simple Linear Engine
- Discrete Choice
- StructuraL Response Prediction Engine to Support Advanced Seismic Risk Assessment
- Two-Stage Degradation Assessment and Prediction Method for Aircraft Engines...
- Machine Learning Theory and Algorithms pp 22-27
- Applying Regression Analysis to Predict and Classify Construction Cycle Time
06 SEP 2022 3. Classification Engines
- A Simple Classification Engine
-subtopic 2
- Applying Regression Analysis to Predict and Classify Construction Cycle Time
- subtopic 2
08 SEP 2022 4. Supervised Learning
- Description
-subtopic 2		 Ch 2
- 2.3-2.7
- subtopic 2
13 SEP 2022 5. Unsupervised Learning
- Description
-subtopic 2		 Ch 2
- 2.8
-subtopic 2
15 SEP 2022 6. Reinforcement Learning
- subtopic1
-subtopic 2		 Ch 3
- Applied Dynamic Programming Bellman and Dreyfus (1962) pp. 1-21
-subtopic 2
20 SEP 2022 7. Exploratory Data Analysis
- Common Data Types
- Visual Exploration
- Downloading data
-
-subtopic 2
22 SEP 2022 8. Probability Distributions
- subtopic1
-subtopic 2
- subtopic1
-subtopic 2
27 SEP 2022 9. Optimization Principles
- subtopic1
-subtopic 2
29 SEP 2022 10. Linear Regression
- subtopic1
-subtopic 2
04 OCT 2022 11. Non-Linear Regression
- subtopic1
-subtopic 2
06 OCT 2022 12. Logistic Regression
- subtopic1
-subtopic 2
11 OCT 2022 13. KNN Classification
- subtopic1
-subtopic 2
13 OCT 2022 14. Decision Tree Classification
- subtopic1
-subtopic 2
Machine Learning by Example
18 OCT 2022 15. Ensemble Learning
- subtopic
-subtopic 2
20 OCT 2022 16. Solution Stacking - I
- Bagging
-subtopic 2
25 OCT 2022 17. Random Forests
- subtopic1
-subtopic 2
27 OCT 2022 18. Solution Stacking - II
- Boosting
-subtopic 2
01 NOV 2022 19. Topic
- subtopic1
-subtopic 2
03 NOV 2022 20. Neural Network Perceptron
- Biological Analogy
- Activation Functions
08 NOV 2022 21. Multi-Layer Perceptrons (MLP)
- subtopic1
-subtopic 2
10 NOV 2022 22. Multinomial Classification by MLP
- subtopic1
-subtopic 2
15 NOV 2022 23. Regression by MLP
- subtopic1
-subtopic 2
17 NOV 2022 24. Image Processing
- subtopic1
-subtopic 2
22 NOV 2022 25. Convolution Neural Networks - I
- subtopic1
-subtopic 2
29 NOV 2022 26. Convolution Neural Networks - II
- subtopic1
-subtopic 2
01 DEC 2022 Project Presentations
- subtopic1
-subtopic 2		 topic name
- subtopic1
-subtopic 2
06 DEC 2022 Project Presentations
- subtopic1
-subtopic 2		 topic name
- subtopic1
-subtopic 2
13 DEC 2022 Exam 3 Due Submit on Blackboard
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