ricky-ma / CPSC340-Machine-Learning

CPSC340: Machine Learning and Data Mining, taught by Frank Wood at UBC, Spring 2019

CPSC340/532M: Machine Learning and Data Mining (Spring 2019)

Course Description

We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the `big data' buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We'll focus on many of the core data mining and machine learning technlogies, with motivating applications from a variety of disciplines.

Assignments

A1

• Linear algebra review
• Probability review
• Calculus review
• Algorithms and data structures review
• Data exploration
• Decision trees

A2

• Training and testing
• Naive Bayes
• K-nearest neighbours
• Random forests
• Clustering

A3

• Finding similar items
• Matrix notation and minimizing quadratics
• Robust regression with gradient descent
• Linear regression and non-linear bases

A4

• Convex functions
• Logistic regression with sparse regularization (L2, L1, L0, L0.5)
• Softmax classification
• One-vs-all logistic regression

A5

• Kernel logistic regression
• Hyperparameter searching
• MAP estimation
• Principal component analysis
• Robust PCA

A6

• Data visualization (PCA, MDS, ISOMAP, t-SNE)
• Stochastic gradient descent for a neural network
• Hyperparameter tuning for a neural network