Lecture Notes



January 4 Lecture 1: Introduction and Lecture 2: Introduction to ML, linear algebra January 7 Finish Lecture 2 and intro to Matlab Matlab Primer
January 11 Matlab and start Lecture 3: kNN classifier January 14 Finish Lecture 3
January 18 Lecture 4: Linear Classifier. Skip slides 64-73. January 21 Finish Lecture 4
January 25 Really Finish Lecture 4 and Lecture 5: Ada Boost January 28 Finish Lecture 5 and Lecture 6: Neural Networks
February 1 Finish Lecture 6 February 4 Lecture 7: Cross Validation
February 8 Lecture 8: Introduction to Natural Language Processing and maybe start Lecture 9: Language Models February 12 Continue Lecture 9
February 22 Short Exam 1 February 25 Continue Lecture 9
March 1 Finish Lecture 9 and Lecture 10: POS tagging March 4 Continue Lecture 10
March 8 Finish Lecture 10 and Lecture 11: Information Retrieval. Ignore everyting after slide 50 (unless you are interested, of course :) March 11 Finish Lecture 11
March 15 Lecture 12: CV: Filtering March 18 Short Exam 2
March 22 Lecture 13: CV: Edge Detection and start Lecture 14: CV: Stereo March 25 Finish Lecture 14
March 29 Lecture 15: CV: Segmentation. April 1 Finish Lecture 15.
April 5 Lecture 16: CV: Motion April 8 SHORT EXAM 3