CSC311H1
UTSGIntroduction to Machine Learning
An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods, decision trees, linear models, and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy.
View full details on the UofT Academic CalendarPrereq: CSC207H1/ CSC207H5/ CSCB07H3; MAT235H1/ MAT235Y1/ MAT237Y1/ MAT257Y1/ (minimum of 77% in MAT130H1/ MAT135H1 and MAT136H1)/ (minimum of 73% in MAT148H1/ minimum of 67% in MAT158H1 and minimum of 73% in MAT149H1/ minimum of 67% in MAT159H1)/ minimum of 73% in MAT137Y1/ minimum of 67% in MAT157Y1; MAT223H1/ MAT240H1; STA237H1/ STA247H1/ STA255H1/ STA257H1 Prerequisite for Faculty of Applied Science and Engineering students: APS105H1/ APS106H1/ ESC180H1/ CSC180H1; MAT291H1/ MAT294H1/ MIE230H1/ (minimum of 77% in MAT186H1, MAT187H1)/ (minimum of 73% in MAT194H1, MAT195H1)/ (minimum of 73% in ESC194H1, ESC195H1); MAT185H1/ MAT188H1; STA286H1/ CHE223H1/ CME263H1/ MIE231H1/ MIE236H1/ MSE238H1/ ECE286H1Breadth: Physical & Mathematical UniversesExcl: CSC411H1, STA314H1, ECE421H1, CSC311H5, CSC411H5, CSCC11H3. NOTE: Students not enrolled in the Computer Science Major or Specialist program at A&S, UTM, or UTSC, or the Data Science Specialist at A&S, are limited to a maximum of 1.5 credits in 300-/400-level CSC/ECE courses.
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Course Info
DepartmentCSC
CampusUTSG (St. George)
Level300
Hours24L/12T
BreadthPhysical & Mathematical Universes
What do you think of CSC311H1?
Reviews (4)
From Reddit
Difficulty: 5/5Usefulness: 5/5
In terms of pure difficulty, this takes the cake as the single hardest course I've taken at UofT. This course combines everything — calculus, CS theory, probability, and linear algebra. I truly believe having a basic understanding of ML is a must in this day and age.
3 weeks ago