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Lecturer: Shashidhar siddagangaiah(沙希達爾)
Email: shashi18@mail.ntou.edu.tw
Phone: (02)2462-2192 #6029
Course ID: M5A015ES
Credits: 3
Objective: The will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part.
Course Prerequisites: Familiarity with linear algebra, probability and programming basics
Outline: After this course, the students will be able to build ML models using Tensorflow. To be able to apply these techniques to new problems and projects
Teaching Method: Lectures and explaining of ML models in Python
Reference:
Shukla, Nishant, and Kenneth Fricklas. Machine learning with TensorFlow. Greenwich: Manning, 2018.
Course Schedule (subject to change):
Week 1: Introduction to Artificial Intelligence (AI)
Week 2: Introduction to neurnos and Machile learning, Introduction to Python
Week 3: Programming in Python with respect to data science
Week 4: Getting started with Tensorflow
Week 5: Role of Tensorflow and python in machine learning
Week 6: Overview of Machine Learning (Process and Techniques)
Week 7: Demonstration of ML concepts with Deep Playground
Week 8: Data Input and Preprocessing with Tensorflow
Week 9: Machine Learning Model Building
Week 10: Prediction with Tensorflow
Week 11: Monitoring and evaluating models using Tensorboard
Week 12:Advanced Tensorflow
Week 13: Convolution neural networks (CNN)
Week 14: CNN model building
Week 15: Scaling up for large datasets
Week 16: Distributed training with hardware accelerators
Evaluation:
Assignments: 50%
Project/Presentation: 50%
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