## Before the practice sessions

Please install Anaconda with Python version 3. You are allowed to use Python 2, but we cannot promise that everything we plan to do will be supported there. After installation make sure you are able to run jupyter notebook. Windows users open Anaconda prompt, other users just run from command line `jupyter notebook`

. This should open a tab in your browser with your folder system. Go to a folder where you want your notebooks to be and create a new notebook by choosing New -> Python (from top right). After the notebook is open type in some commands like

import pandas as pd import numpy as np import sklearn print("hello")

and press SHIFT + ENTER. If everything works then you are ready for the practice session. If some of the packages cannot be imported run `conda install package-name`

to install the necessary packages.

If you have problems with this then write to the course forum in Piazza!

## Practice Sessions

#### Practice 01 - Sep 9-13 - Basic linear classifier & Perceptron

Exercises, solutions, solutions as a Jupyter notebook

#### Practice 02 - Sep 16-20 - KNN & Naive Bayes

Exercises, Slides, solutions, solutions as a Jupyter notebook

#### Practice 03 - Sep 23-27 - Linear regression and regularization

Exercises (pdf), Exercises + solutions (colab)

#### Practice 04 - Sep 30-Oct 4 - Perceptron in dual form & SVM

Exercises (pdf), Solutions (colab)

#### Practice 05 - Oct 7-11 - Kernel methods

#### Oct 14 - TEST 1

#### Practice 06 - Oct 21-25 - Decision trees

#### Practice 07 - Oct 28-Nov 1 - F1 measure & ROC & Feature engineering

#### Practice 08 - Nov 4-8 - Logistic regression

#### Practice 09 - Nov 11-15 - Backpropagation & Softmax

#### Nov 18 - TEST 2

#### Practice 10 - Nov 25-29 - Ensemble methods

#### Practice 11 - Dec 2-6 - Probabilistic graphical models

#### Practice 12 - Dec 9-13 - Bayesian machine learning

Exercises, Notebook, Solutions 1 (pdf), Solutions 2 (notebook)