175 - Elements of Machine Intelligence
 

 

             

This course provides an undergraduate-level introduction to Statistical Learning. It address problems such as classification and detection, parameter and model estimation, or clustering, which are common in signal processing, communications, image processing, computer vision, artificial intelligence, speech analysis and recognition, data-mining, computational biology, bio-informatics, etc.

Lectures: TuTh, 5:00p-6:20p, WLH2208
Instructor: Nuno Vasconcelos
n u n o @ e c e . u c s d . e d u, EBU1-5602
office hours: Thursday, 6:30p-7:30p
Teaching Assistant: Vijay Mahadevan, v m a h a d e v @ u c s d . e d u
 
Office hours:5.30-6.30pm Monday, EBU1 5706
Text: Introduction to Machine Learning
Ethem Alpaydin, MIT Press, 2004
Syllabus: [ps, pdf]
Homework: Problem set 1 [ps, pdf]
Problem set 2 [ps, pdf, data]
Problem set 3 [ps, pdf]
Problem set 4 [ps, pdf, data]
Problem set 5 [ps, pdf]
Problem set 6 [ps, pdf]
Problem set 7 [ps, pdf, libsvm, instructions]
Topics: Lecture 1: introduction [slides]
Lecture 2: review of linear algebra [slides]
Lecture 3: review of probability [slides]
Lecture 4: metrics, whitening, nearest neighbors [slides]
Lecture 5: Bayes decision rule [slides]
Lecture 6: Bayes decision rule [slides]
Lecture 7: Maximum Likelihood Estimation [slides]
Lecture 8: Saliency and Object Recognition (by Dashan Gao)
Lecture 9: mid-term review [problems,solutions]
Lecture 10: mid-term
Lecture 11: ML Estimation [slides]
Lecture 12: Least Squares [slides]
Lecture 13: Least Squares [slides]
Lecture 14: Clustering, k-means, EM [slides]
Lecture 15: Image Annotation and Retrieval (by Nikhil Rasiwasia)
Lecture 16: Dimensionality [slides]
Lecture 17: Principal component analysis [slides]
Lecture 18: Hyperplane Classifiers [slides]
Lecture 19: Kernels [slides]
Lecture 20: Support Vector Machine [slides]
Lecture 21: Traffic classification (by Antoni Chan)