CS178: Machine Learning and Data Mining


Assignments and Exams:


Lecture: ICS 259, TR 2pm-3:30pm

Discussion: Bren Hall 1500, W 4-5pm

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

  • Office Hours: 2:00-3:00pm Mondays, Bren Hall 4066, or by appointment

Course Notes in development

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.

Textbook and Reading

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


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). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

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 and Schedule (may be updated)

  • L01 (PDF): Introduction; classification & regression; nearest neighbor methods
  • R01 (PDF): Matlab basics
  • L02 (PDF): Linear regression
  • L03 (PDF): Linear regression, overfitting, regularization
  • L04: no class
  • L05 (PDF): Classification, probability, decisions
  • R03 (PDF): Matlab classes, Probability
  • L06 (PDF): Bayes classifiers, Naive Bayes
  • L07 (PDF): Perceptrons, Logistic regression
  • R04: Matlab, homework discussion
  • L08 (PDF): Multi-layer perceptrons (neural networks); decision trees
  • L09 (PDF 1, PDF 2): VC dimension, Decision trees, Ensemble methods
  • Guest lecture: David Newman, latent space models
  • Decision trees; ensemble methods (bagging, boosting) (see L09-2)
  • Midterm exam: Past years exams: 2011(soln), 2010(soln)
  • L12, (PDF) Clustering
  • L13, (PDF) Clustering, latent space representations, collaborative filtering
  • L14, (PDF) Probability models for unsupervised learning
  • L15, (PDF) Probability models, data mining
  • L16, (PDF) Support vector machines
  • L17, (PDF) Time series, Markov chains, AR models
  • L18, (PDF) Graphical models
  • Final exam: Past years exams: 2011(soln), 2010 (soln)

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


Your course project is in the nature of an "undirected" homework assignment. Choose a machine learning problem, on your own or from the list below, and explore the implied prediction task to the best of your ability. You can try different learners, choosing from methods we have used in the homework or implementing new ones; different feature representations (feature selection or augmentation); meta-learning algorithms such as bagging and boosting; and hold-out or cross-validation assessment techniques. You should explore the problem in some detail, describing the different ideas you tried and how (and whether or not) they worked, and how you assessed their performance.


  • Face detection: this zip file contains a dataset of 24x24 pixel image patches containing faces and non-faces. Learn to predict the presence of a face. Also included is a function for computing the Haar wavelet features and a simple demo of adaBoost, the building blocks of the Viola-Jones technique.
  • Collaborative filtering: learn to predict how you will rate something, given how others have rated it. This zip file contains a subset of the "Jokes" database for collaborative filtering, in which viewers have rated a subset of jokes on their amusement value, as well as some simple demo code of and SVD-based estimate. I suggest combining it with nearest-neighbor approaches, and trying different levels of complexity (latent space dimension, neighbors, weighting functions, etc.) in your predictors.
  • Web ranking: learn to predict the relative relevance of a set of returned webpages; this problem is of great importance in both search and advertising, the mainstays of many internet companies. Unfortunately I cannot redistribute the data myself, but you can download Yahoo's ranking challenge data at http://webscope.sandbox.yahoo.com/catalog.php?datatype=c. Here is a zip file with some code for reading their data, which come in "queries" (a web search) with a variable length list of possible responses and their human-rated quality. (See "main.m" for some example code.)

Last modified February 13, 2017, at 02:23 PM
Bren School of Information and Computer Science
University of California, Irvine