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, 12:30p-01:50p, CENTR-214
     
  Instructor: Nuno Vasconcelos
    n u n o @ e c e . u c s d . e d u, EBU1-5602
    office hours: Friday, 9:30a-10:30a
     
  Teaching Assistant: Zheng Wei, zhw035 @ u c s d . e d u
Akshaya Purohit, akpurohi @ e n g . u c s d . e d u  
  Office hours: TBA  
  Discussion: Wed, 1-1:50PM, CENTR-105
     
  Text: Introduction to Machine Learning
    Ethem Alpaydin, MIT Press
     
  Syllabus: [pdf]
     
  Homework: Problem set 1 [pdf]                                              Not due
    Problem set 2 [pdf, data]                                      Issued: 1/19  Due: 1/26
    Problem set 3 [pdf]                                              Issued: 1/26  Due: 2/2
    Problem set 4 [pdf, data]                                      Issued: 2/11  Due: 2/23
    Problem set 5 [pdf]                                              Issued: 2/23  Due: 3/2
    Problem set 6 [pdf]                                              Issued: 3/2    Due: 3/9
    Problem set 7 [pdf, libsvm, instructions, example]      Issued: 3/9  Due: 3/16
     
  Topics: Lecture 1: introduction [slides]
    Lecture 2: review of linear algebra [slides]
    Lecture 3: review of linear algebra (continued)
    Lecture 4: review of probability [slides]
    Lecture 5: metrics, whitening, nearest neighbors [slides]
    Lecture 6: Bayes decision rule [slides]
    Lecture 7: Bayes decision rule [slides]
    Lecture 8: Bayes decision rule (continued)
    Lecture 9: mid-term review [problems,solutions]
    Lecture 10: mid-term
    Lecture 11: Maximum Likelihood Estimation [slides]
    Lecture 12: MLE & Regression[slides]
    Lecture 13: MLE & Regression (continued)
    Lecture 14: Least Squares [slides]
    Lecture 15: Clustering, k-means [slides]
    Lecture 16: Clustering, EM [slides]
    Lecture 17: Principal component analysis [slides]
    Lecture 18: Kernels [slides]
    Lecture 19: Support Vector Machine [slides]
    Lecture 20: Support Vector Machine [slides]