CS178 Fall 2020

CS178: Machine Learning & Data Mining

University of California, Irvine, Fall 2020

Prof. Alexander Ihler

How can a machine learn from experience, to become better at challenging tasks? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms combine techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data to make predictions or decisions without human intervention.

The field of machine learning is now pervasive, with applications from the web (search and advertising) to national security, from analyzing biochemical interactions to traffic and climate. The $1M Netflix prize stirred interest in learning algorithms among professionals, students, and hobbyists; now, websites like Kaggle host regular open competitions on many companies' data.  This course will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry applications of machine learning.

Course Materials

• Piazza will be used for all course announcements, discussions, and questions.  All enrolled students should sign up, watch for important announcements, and post questions (anonymously if you prefer) about course content.
• Lecture Calendar, including readings, slides, and example code
• Discussion Calendar, including example code notebooks
• Homework Assignments and Exams
• Homework Policies and Resources, including the Collaboration and Academic Honesty Policy
• Python Resources for homework assignments
• Projects

Course Information

• Lectures: Tues/Thurs from 11:00-12:30pm via Zoom (meeting # 953-3059-4877, password 178178).  Lectures will also be recorded and links provided for later review.
• Discussions: Thursday (5/6/7/8pm) and Friday (8/9am) via Zoom (same as lecture)
• Instructor:  Prof. Alexander Ihler.  Office hours: Fridays 11am, or right after Tuesday lecture (via same zoom), or by appointment (request via private post on Piazza)
• TAs:
  • Tiancheng Xu. Office hours: Wednesday 5:30-6:30pm
  • Takashi Nagata.  Office hours: Thursday 4-5pm
  • Antonios Alexos. Office hours: Monday 3-4pm
• Reader: Hin Wai Lui. 

Course Prerequisites

We will assume basic familiarity with the concepts of probability and linear algebra. Homework assignments require some Python programming experience, and knowledge about basic data structures and algorithms.

Reference Materials

There is no required textbook for the class.  The lecture calendar links to supplemental notes for most topics.  Lectures are generally self-contained, but for additional background reading, the following references are freely available online:

• Daume, A Course in Machine Learning
• Barber, Bayesian Reasoning and Machine Learning.
• Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning.
• MacKay, Information Theory, Inference, and Learning Algorithms.

The following print textbooks are good quality, but may emphasize different aspects of ML than our course:

• Bishop, Pattern Recognition and Machine Learning.
• Murphy, Machine Learning: A Probabilistic Perspective.
• Duda, Hart, and Stork, Pattern Classification.
• Rogers and Girolami, A First Course in Machine Learning.
• Mitchell, Machine Learning.

Exams and Course Grades

Overall course grades will be assigned as follows: 25% homeworks, 15% projects, 25% midterm exam, 35% final exam. The midterm exam will be available online through Canvas, on Tuesday, Nov 10.  The final exam will also be online, and given on Tue, Dec 15. Exams must be taken on those days. Exceptions are granted only for medical or family emergencies.