Links to the course material will be provided in the schedule below after each class. You may want to have a look at the previous edition of the course for reference.

The course schedule

Week	Monday	Wednesday	Friday
01	Apr 13 No class	Apr 15 No class	Apr 17 No class
02	Apr 20 Dept. meeting - no class	Apr 22 Introduction  [slides, 8up]	Apr 24 Math preliminaries  [slides, 8up, notes]
03	Apr 27 Probability theory  [slides, 8up, notes]	Apr 29 Information theory  [slides, 8up, notes]	May 01 No class
04	May 04 Machine learning introduction  [slides, 8up, notes]	May 6 ML intro (contd.)	May 8 Classification  [slides, 8up, notes]
05	May 11 Classification (contd.)	May 13 ML evaluation  [slides, 8up, notes]	May 15 lab  [slides]
06	May 18 ML evaluation (contd.)	May 20 ANN basics  [slides, 8up, notes]	May 22 lab
07	May 25 Unsupervised learning  [slides, 8up, notes]	May 27 Summary	May 29 lab  [slides]
	Jun 01 Sem. break	Jun 03 Sem. break	Jun 05 Sem. break
08	Jun 08 Sequence learning  [slides, 8up, notes]	Jun 10 Sequence learning	Jun 12 lab
09	Jun 15 Recurrent and convolutional networks  [slides, 8up, notes]	Jun 17 Recurrent and convolutional networks	Jun 19 lab
10	Jun 22 Language models  [slides, 8up]	Jun 24 Language models	Jun 26 lab
11	Jun 29 Tokenization / segmentation  [slides, 8up]	Jul 01 POS tagging and morphology  [slides, 8up]	Jul 03 lab
12	Jul 6 Dense vector representations  [slides, 8up]	Jul 8 Text classification  [slides, 8up]	Jul 11 lab
11	Jul 13 Parsing I  [slides, 8up]	Jul 15 Parsing II	Jul 17 lab
14	Jul 20 State of the art in NLP	Jul 22 Exam preparation/discussion	Jul 24 Exam  [exam, data]