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 15 No class	Apr 17 Introduction  [slides, 8up] Reading: None	Apr 19 No class
02	Apr 22 No class	Apr 24 Math preliminaries  [slides, 8up] Reading: None	Apr 26 Probability theory  [slides, 8up] Reading: None
03	Apr 29 Probability theory (contd.)	May 01 No class	May 03 Information theory  [slides, 8up]
04	May 06 Machine learning intro  [slides, 8up] Reading: HTF 3.2 & 3.4	May 08 lab - Assignment 1  [recap slides (05.06)]	May 10 Classification I  [slides, 8up] Reading: JM 6.6 (JM3 Ch.7), HTF 4.4
05	May 13 Classification II	May 15 lab	May 17 ML evaluation  [slides, 8up]
06	May 20 ANN basics  [slides, 8up] Reading: JM3 Ch.7, HTF Ch.11	May 22 lab - Assignment 2	May 24 ANN basics
07	May 27 Unsupervised learning  [slides, 8up] Reading: HTF Ch.14	May 29 lab	May 31 Unsuprevised learning
08	Jun 03 Unsupervised learning	Jun 05 lab - Assignment 3	Jun 07 Sequence learning  [slides, 8up]
	Jun 11 Sem. break	Jun 13 Sem. break	Jun 15 Sem. break
09	Jun 17 Recurrent and convolutional networks  [slides, 8up]	Jun 19 lab - Assignment 4	Jun 21 Language models  [slides, 8up]
10	Jun 24 Tokenization / segmentation  [slides, 8up]	Jun 26 lab	Jun 28 POS tagging  [slides, 8up]
11	Jul 01 Dense vector representations  [slides, 8up]	Jul 03 lab - Assignment 5	Jul 05 Dense vector representations
12	Jul 08 Text classification  [slides, 8up]	Jul 10 lab	Jul 13 Parsing I  [slides, 8up]
11	Jul 15 Parsing II	Jul 17 lab - Assignment 6	Jul 19 Summary  [slides, 8up]
14	Jul 22 Exam preparation/discussion	Jul 24 lab - Assignment 7	Jul 26 Exam