CS178: Machine Learning and Data Mining


Assignments and Exams:

Project 3/21/14 
FinalFri 8:00-10:00am3/21/14  

Lecture: Mon/Wed/Fri 11am-12pm, ICS 174

Discussion: Monday 4-5pm, Eng Tower (ET) 204

Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

  • Office Hours: Wed 2:30-3:30pm, Bren Hall 4066, or by appointment

Teaching Assistant: Moshe Lichman (mlichman@uci.edu)

  • Office Hours: Thu 3:30-4:30pm, Bren Hall 4059 or by appointment

Course Notes in development

Also, a possibly helpful LaTeX template I use for homeworks and solutions. (Or, this link has another nice way to include Matlab code in LaTeX.)

Introduction to machine learning and data mining

How can a machine learn from experience, to become better at a given task? 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 use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.


We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.)

Textbook and Reading

There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".


I use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/winter2014/cs178.


Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). If you use Octave, please be careful to use Matlab-compatible syntax (not Octave extensions), since otherwise I or the TA may be unable to interpret your code.

If you are not familiar with Matlab, there are a number of tutorials on the web:

You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

Interesting stuff for students

  • tba...

Syllabus (subject to change)

PDF1 , 2 , 3 , 4Introduction
PDF1 , 2Nearest neighbor methods
PDF1 , 2Bayes classifiers, naive Bayes
PDF1 , 2 , 3 , 4 , 5 , 6Linear regression
PDF1 , 2Linear classifiers; perceptrons & logistic regression
PDF1VC dimension, shattering, and complexity
PDF Neural networks (multi-layer perceptrons) and deep belief nets
PDF Support vector machines; kernel methods
PDF1 , 2Decision trees for classification & regression
PDF1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
PDF Unsupervised learning: clustering methods
PDF1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations

Previous year's lectures (2012b, 2012a, 2011, 2010) are also available.

Course Project

  • tba

Last modified January 19, 2015, at 04:34 PM
Bren School of Information and Computer Science
University of California, Irvine