COMP 451 - Fundamentals of Machine Learning
COMP 451 - Fundamentals of Machine Learning McGill's introductory course in machine learning Home Syllabus Schedule Tentative schedule for COMP 451 Lec. Date Topic Reading Additional Info 1 Jan. 7 Introduction to machine learning Lecture notes (Chap 1) Jamboard Mandatory reading: This paper. Part I: Decision Boundaries 2 Jan. 12 Instance-based learning Lecture notes (Chap 2) Jamboard 3 Jan. 14 Parametric Learning and Perceptrons (Part I) Lecture notes (Chap 3; Sec 3.1-3.2) Jamboard 4 Jan. 19 Parametric Learning and Perceptrons (Part II) Lecture notes (Chap 3; Sec 3.3) Jamboard Part II: Likelihood 5 Jan. 21 Maximum Likelihood Lecture notes (Chap 4) Jamboard 6 Jan. 26 Naive Bayes (Part I) Lecture notes (Chap 5; Sec 5.1-5.3) Jamboard 7 Jan. 28 Naive Bayes (Part II) Lecture notes (Chap 5; Sec 5.3-5.5) Jamboard Theory Assignment 1 Released Practice Assignment 1 Practice Assignment 1 (with solutions) Assignment 1 [Theory Assignment 1] (due
Tentative schedule for COMP 451 Lec. Date Topic Reading Additional Info 1 Jan. 7 Introduction to machine learning Lecture notes (Chap 1) Jamboard Mandatory reading: This paper. Part I: Decision Boundaries 2 Jan. 12 Instance-based learning Lecture notes (Chap 2) Jamboard 3 Jan. 14 Parametric Learning and Perceptrons (Part I) Lecture notes (Chap 3; Sec 3.1-3.2) Jamboard 4 Jan. 19 Parametric Learning and Perceptrons (Part II) Lecture notes (Chap 3; Sec 3.3) Jamboard Part II: Likelihood 5 Jan. 21 Maximum Likelihood Lecture notes (Chap 4) Jamboard 6 Jan. 26 Naive Bayes…
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