January 4 | Lecture 1: Introduction and Lecture 2: Introduction to ML, linear algebra | January 7 | Finish Lecture 2 and intro to Matlab Matlab Primer | ||
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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 | ||