CMPSC 448 Spring 2024: Machine Learning

Course Information

Instructor

Rui Zhang

Lecture Time and Location

MWF, 9:05-9:55 am @ Animal, Vet and Biomed Sci 102

Sarkar Snigdha Sarathi Das
Ryo Kamoi

Course Syllabus

Detailed syllabus is avaialbe here.

Course Goals and Objectives

The goal of this course is to introduce data analysis from the machine learning perspective, in particular how to design and evaluate data-driven solutions for real problems in different domains. Students will gain familiarity with the workings of common machine learning models and will learn how noise and bias in the data affect their results. The course assumes programming skills in Python and knowledge in linear algebra, calculus, basic probability and statistics.

Prerequisites

STAT 318 or STAT 414 and CMPSC 122 or prior programming experience. You are expected to have a good understanding of Linear Algebra, Multivariate Calculus, Probability and Statistics, and Programming Skills. We will cover some background material on these topics early in the lectures. However, it is not meant to replace these regular prerequisite courses. For programming skills, you are expected to feel comfortable processing and analyzing data in Python and be familiar with basic algorithmic design and analysis.

Textbook

(CIML)A Course in Machine Learning by Hal Daumé III.
(RLAI)Reinforcement Learning: An Introduction by Sutton and Barto.
(MML)Mathematics For Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong.
Pattern Recognition and Machine Learning by Christopher Bishop.
Machine Learning: A Probabilistic Perspective by Kevin Murphy.
Elements of Statistical Learning by Hastie, Tibshirani, Friedman.

Course Schedule

Date	Topic	Material / Reading	Event
Part 1: The Basics of Machine Learning and Background
Week 1 Monday Aug 26	Introduction & Logistics	CIML 1.1, 1.2, 1.4
Wednesday Aug 28	The Processes of Learning	CIML 2
Friday Aug 30	The Processes of Learning	CIML 2, 5.6, 5.9	HW 1 Out