Understanding Machine Learning: From Theory to Applications (DATASCI 224)

Spring 2024 (3 units)

This course teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.

 

Online Syllabus

Objectives

At the conclusion of this course, students will be able to:

  • Explain the key mathematical ideas that underly different machine learning algorithms.
  • Demonstrate proficiency in applying new machine learning algorithms.
  • Select the most appropriate machine learning algorithm/analysis strategy to answer their questin of interest.
  • Critique and analyze applications of machine learning algorithms.

 

Prerequisites


Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (BIOSTAT 216) 

This course is part of the Health Data Science Masters and Certificate Program and may have space limitations.  Auditing is not permitted.


Exceptions to this prerequisite may be made with the consent of the Course Director, space permitting.

Faculty

Course Director:

Jean Feng, PhD, MS

Title: Assistant Professor
Department of Epidemiology & Biostatistics
email: [email protected]

Format

Lectures will be held at 1:15 - 2:45 PM and Labs held 2:45 - 3:45 PM, Thursdays, April 4 through June 6.

Each week, new material is introduced via an interactive lecture and recommended readings. Learning is reinforced via computer labs, structured discussion sections, and homework.

The schedule for the quarter shows dates and times for all activities. All course materials and handouts will be posted on the course's online syllabus.

Materials

Materials needed:

Software Python

Grading

Grades will be based on total ponts achieved on the homework assignments and class project. Please note that late assignments will not be acceptd.

Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.

UCSF Graduate Division Policy on Disabilities

To Enroll

Matriculated students (ATCR/MAS/CiHDaS/MiHDaS/PhD) use the Student Portal.

Course Fees
How to pay (please read before applying)
Only one application needs to be completed for all courses desired during the quarter.

Spring 2024 Course Schedule

Apply by March 31, 2024