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]