Machine Learning I

Course Code :2004FLWDTA
Study domain:Linguistics and Proficiency
Academic year:2020-2021
Semester:1st semester
Contact hours:12
Credits:3
Study load (hours):84
Contract restrictions: No contract restriction
Language of instruction:English
Exam period:exam in the 1st semester
Lecturer(s)Walter Daelemans

3. Course contents *

  1. Introduction, brief history, regression-classification, supervised-unsupervised-reinforcement learning, theoretical background: minimal description length, compression, PAC-learnability, version spaces, bias-variance
  2. Methodology: sampling, (embedded) cross-validation, stratification, normalization, tf-idf preprocessing
  3. Methods 1: rule induction, decision trees/forests
  4. Methods 2: k-nn, clustering
  5. Methods 3: linear & logistic regression
  6. Methods 4: naive bayes, svm