2024fall_Python Machine Learning Application Technology_Department of Electronic Engineering 4 A
Course Period：Now ~ Any Time
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Course Intro
Course Plan
教學目標：This course is intended to provide students with the fundamental concepts and algorithms in Machine Learning. Besides, students could understand and know how to implement solutions to real-world machine learning problems by working through case study predictive modeling problems in Python. We will cover various topics, including decision trees, logistic regression, support vector machines, ensemble methods, Bayesian methods, neural networks, clustering, and dimensionality reduction.
Textbooks
1. Machine Learning: The Art and Science of Algorithms that Make Sense of Data (2012), Peter Flach, Cambridge University Press. 2. Self-made teaching material.
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01. Introduction
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02.Environment for Training Machine Learning Algorithms
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03.Basic Knowledge in Python
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04. Python Ecosystem for MachineLearning
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05.Understand Machine Learning Data
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06.Math for machine learning
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07.Your first step towards Machine Learning
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08.Logistic Regression by using Gradient Descent Algorithm
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09.K-Nearest Neighbors Algorithm
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10. K-means Clustering
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11.Image recognition with Machine Learning
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12.Scikit-Learn - Building a Machine Learning Model in Python for Image Classification
Teacher / 黎玉線
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