Machine Learning

lecture: Fridays, 13.15--15.00, A-1/204;
lab: Fridays, 15.15--16.55, C-11/4.09.

Lab on Dec 2: on Zoom, Meeting ID: 958 5519 3305. (Password was given on the lecture, an abbreviation consisting of three capital letters.)

Time slots for the 'oral exam' (Zoom meeting data above):

Learning materials

Files/links used in lectures

Office hours

tba

Assignment 1

linear regression (0.5 points) and Ridge (iterative method: 0.5 points). Deadline: labs during the week 17--21 October.

Assignment 2

Write neural network from scratch to deal with a classification problem of your choice. It is enough to implement a 2-layer dense neural network and play with some of the hyperparameters. With a good implementation you may easily take more layers and experiment with the number of layers as well, but it is not obligatory.
Suggestions: use the MNIST database with handwritten digits (see below); use Layer as a basic building block so that you may use efficient matrix operations.
You may look at the forward pass/backpropagation example, sort of hand-calculated using very small 2x2 network (corrected on Jan 13, there were some numerical mistakes at dL/dW).

Database with handwritten digits:
you may take the files mnist_loader.py and mnist.pkl.gz from this repository. Please read the documentation in mnist_loader.py to learn how to use it (it's simple!).

Assignment 3

Perform some experiments with convolutional neural networks, for example using Keras.
Deadline: January 13.

Assignment 4

Perform some experiments with recurrent neural networks, for example using Keras.
Deadline: January 20.

Assignment 5

Implement alpha-beta algorithm, or Monte Carlo Tree Search, or Nalimov tables (one of these) for a game of your choice (not every game is suitable). If you prefer, you may also do something else concerning Machine Learning, on the subject of your choice -- however, the subject has to be accepted by me *beforehand* (by email).
Deadline: February 3.

Rules
up