Description:
Course Description: This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on real-world examples in Python. The overall aim is to provide the students with a good understanding of machine-learning technologies, building machine learning with Python, and applying machine-learning technologies to address real-world problems.
Prerequisites: None
Learning Outcome:
– Explain the relationship (main mechanisms, internal logic, computing components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoder, General Adversarial Network (GAN), and Reinforcement Learning (RL))
– Program the basic realization of the machine learning models, stated in Learning Objective 1, in Python
– Apply the eight machine learning models stated in Learning Objective 1 to solve real-world problems
| Module |
Topic & Readings |
| Module 1 |
What is Machine Learning
What is the Insight of Machine Learning |
| Module 2 |
Linear Regression Application
Linear Regression Implementation |
| Module 3 |
Logistic Regression Formulation and Implementation
Solving the Robust Regression Problem |
| Module 4 |
Introduction of Neural Networks Application
Fully Connected Neural Network Implementation |
| Module 5 |
Convolutional Neural Network Application
Convolutional Neural Network Implementation |
| Module 6 |
Recurrent Neural Network Application
Recurrent Neural Network Implementation |
| Module 7 |
Auto-Encoder Application and Implementation
Generative Adversarial Network Application and Implementation
Deep Reinforcement Learning Models Application and Implementation |
Faculty: Jin Kocsis