Description

This course begins with a brief review of linear algebra, probability, and statistics, and then introduces both algorithms and applications of machine learning. The course covers the basic algorithms of supervised maching learning and unsupervised learning, including linear regression, neural networks, decision trees, and support vector machines. Through the course, we will get familiar with applications in computer vision and natural language processing, such as image classificaton and large language models.

Lectures

• Monday, 2:30 PM - 3:45 PM, COB1 Room 116
• Friday, 2:30 PM - 3:45 PM, COB1 Room 116

Office hours and contact information

• Instructor: Meng Tang (Email: mtang4@ucmerced.edu)
• Office hours by the instructor: Every Monday from 1:30 PM to 2:30 PM, SE2 room 279
• Teaching Assistant: Fang Chen (Email: fchen20@ucmerced.edu)

Topics

• Statistical Learning and Bayesian decision theory
• Density estimation
• Clustering and Mixture of Gaussians
• Dimensionality reduction
• Logistic Regression and generalized linear models
• Perceptron and multilayer neural networks
• Convolutional Neural Networks
• Decision trees and random forests
• Ensemble learning: bagging and boosting
• Kernel machines (support vector machines, SVMs)

Text books

• Hal Daume III, Course in Machine Learning (2017), http://ciml.info
• Kevin Murphy, Probabilistic Machine Learning: An Introduction (2022), https://probml.github.io/pml-book/book1.html
• Understanding Deep Learning by Simon J.D. Prince Published by MIT Press 2023. https://udlbook.github.io/udlbook

Prerequisites

• CSE100 Algorithm Design and Analysis
• MATH24 Linear Algebra and Differential Equations or equivalent course
• MATH32 Probability and Statistics or equivalent course
• MATH141 Linear Analysis I
• Python programming skills

Grading

• Exams (40%)
  • Midterm exam 20%
  • Final exam 20%
• Assignment (40%)
  • Assignment 1 10%
  • Assignment 2 10%
  • Assignment 3 10%
  • Assignment 4 10%
• Labs (20%)