updated readme, get rid of old theoretical compilation

README.md

@@ -1,47 +1,39 @@
 # Machine Learning at MIPT
-This course aims to introduce students to contemporary state of Machine Learning and Artificial Intelligence. It is designed to take one year (two terms at MIPT) - approximately 2 * 15 lectures and seminars.
+This course aims to introduce students to modern state of Machine Learning and Artificial Intelligence. It is designed to take one year (two terms at MIPT) - approximately 2 * 15 lectures and seminars.
 
-All materials are available here, the complementary website available at [`ml-mipt.github.io`](https://ml-mipt.github.io/)
+All learning materials are available here, full list of topics considered in the course are listed in `program_*.pdf` files
 
-## `Important` current repository structure
+Organizational information about current launches available at [`ml-mipt.github.io`](https://ml-mipt.github.io/)
+
+## Repository structure
 
 * on `master` branch previous term materials are stored
     to give a quick and comprehensive overview
 * on `basic` and `advanced` branches materials for
     current launches are being published
-
-Later (after the term ends) we will merge a new state to master as `fall_2019`.
-
-## Current launches
-
-As of Fall 2019 we have two tracks: [`basic`](basic.md) and [`advanced`](advanced.md).
+* tags (e.g. `spring_2019`) contain previous launches materials for convenience
 
 ## Video lectures
 
 * basic track (Spring 2019): [`youtube playlist`](https://www.youtube.com/playlist?list=PL4_hYwCyhAvasRqzz4w562ce0esEwS0Mt)
-* advanced track (Fall 2019, in progress): [`youtube playlist`](https://www.youtube.com/playlist?list=PL4_hYwCyhAvZeq93ssEUaR47xhvs7IhJM)
+* advanced track (Fall 2019): [`youtube playlist`](https://www.youtube.com/playlist?list=PL4_hYwCyhAvZeq93ssEUaR47xhvs7IhJM)
 
 ## Prerequisites
 
 We are expecting our students to have a basic knowlege of:
-* calculus, especially matrix calculus
+* calculus, especially matrix calculus, differentiation
+* linear algebra
 * probability theory and statistics
 * programming, especially on Python
 
 Although if you don't have any of this, you could substitude it with your diligence because the course provides additional materials to study requirements yourself.
 
-## Theoretical and extra materials
+## Extra theoretical materials
 
-Informal "aggregation" of all topics by previous years students: [file](https://github.com/ml-mipt/ml-mipt/blob/master/ML_informal_notes.pdf) (in Russian).
+Informal "aggregation" of all topics by previous years students: [file](https://github.com/ml-mipt/ml-mipt/blob/spring_2019/ML_informal_notes.pdf) (in Russian) - useful for fast and furious exam passing
 
-## Docker image
+Also lectures and seminars contains references to more detailed materials on topicks
 
-If conda/pip doesn't work, consider using Docker.
-Due to the root privileges in the docker contaner we do not recommend to use it in open networks, it may make your systerm vulnerable. The instructions will be updated in future.
+## Docker image
 
-1. Install Docker CE from the [official site](https://www.docker.com/products/docker-desktop)
-2. In your command line run: 
-```bash
-sudo docker run -d -p 4545:4545 -v <your_local_path>:/home/user vlasoff/ds jupyter notebook
-```
-3. Open your browser on `localhost:4545`
+Using docker for tasks evaluation is a good idea, prebuilt image is under cunstruction