Course Syllabus

CS178: Machine Learning & Data Mining

University of California, Irvine, Fall 2018

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

• Campuswire 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 and slides
• Discussion Calendar, including links to Jupyter notebooks
• Homework Assignments and Exams
• Homework Policies and Resources, including the Collaboration and Academic Honesty Policy
• Python Resources for homework assignments
• Projects are based on an in-class Kaggle competition
  

Course Information

• Lectures:  Mondays and Wednesdays and Fridays from 11:00-11:50am, Social Science Lecture Hall (SSLH) 100.
• Discussions:  Wednesdays 12:00 / 1:00 / 4:00 / 5:00, Social Science Lab (SSL) 140
  
• Instructor:  Prof. Alexander Ihler.  Office hours: Tues 1:00-2:00 PM, DBH 4066.
• Head Teaching Assistant: Tiancheng Xu. Office Hours: Thursdays 12:30 - 1:30 PM, ICS 464B
• Teaching Assistant: Abhishek Jindal. Office Hours: Wednesdays 02:30 - 03:30 PM, ICS 464C
  
• Reader: Zhanhang Liang
• Reader: Chirag Choudhary
  
• Reader: Ananthakrishnan Pushpendran

Course Prerequisites

An introductory course in probability and statistics (STATS 67). Courses in calculus (MATH 2B), linear algebra (MATH 3A or I&C SCI 6N), and discrete mathematics (I&C SCI 6B, I&C SCI 6D). Homework assignments require some Python programming experience, and knowledge about basic data structures and algorithms.

Reference Materials

There is no required textbook for CS178.  The lecture calendar links to supplemental notes for some 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 in some cases more advanced or mathematical than this 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 given during the normal 11:00am lecture time on (tbd). The final exam will be given on (tbd). Exams must be taken at these times. Exceptions are granted only for medical or family emergencies.

During exams, use of electronic devices is not allowed. However, you are allowed to bring a single, 8.5x11-inch page (both sides) of handwritten notes.