Machine Learning Methods

Course contents for Machine Learning Methods (MATH 4388/5388 at University of Colorado Denver)

Chapter 1: Machine Learning Landscape

• Definition of machine learning
• Components and types of machine learning systems
• Fundamental concepts behind machine learning
• Challenges of machine learning
• Review of two libraries: NumPy and Pandas

Chapter 2: End-to-End Machine Learning Project

• Get the data
• Explore the data (Exploratory Data Analysis or EDA)
• Prepare the data for machine learning algorithms
• Select and fine-tune a model

Chapter 3: Classification

• Confusion matrix
• Precision and recall
• F1 score
• Precision/recall curve
• Receiver Operating Characteristic (ROC) curve
• Multiclass classification

Chapter 4: Training Models

• Linear regression: Problem formulation, assumption, loss function, gradient
• Normal equation
• Scikit-learn implementation
• Evaluation metrics
• Gradient descent (GD) and variants
• Nonlinear extension and regularization
• Logistic regression

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

• K-means clustering
• DBSCAN
• Gaussian mixture model