Arvutiteaduse instituut
  1. Kursused
  2. 2018/19 sügis
  3. Masinõpe (MTAT.03.227)
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Masinõpe 2018/19 sügis

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  • Lectures
  • Practice sessions
  • Homeworks
  • Links

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 10-14 - Basic linear classifier & Perceptron

Practice 02 - Sep 17-21 - KNN & Naive Bayes

Slides

Slides ver2

Jupyter notebook

Jupyter notebook ver2

Practice 03 - Sep 24-28 - Linear regression and regularization

Jupyter notebook

Jupyter notebook ver2

Practice 04 - Oct 1-5 - Perceptron in dual form & SVM

Exercises, Notebook

Practice 05 - Oct 8-12 - Kernel methods

Oct 15 - TEST 1

Practice 06 - Oct 22-26 - Decision trees

Exercises, Notebook

Practice 07 - Oct 29-Nov 2 - F1 measure & ROC & Feature engineering

ROC explained
Evaluation measures and ROC curve
Evaluation measures and ROC curve Jupyter notebook file

Practice 08 - Nov 5-9 - Logistic regression

Exercises, Notebook

Practice 09 - Nov 12-16 - Backpropagation & Softmax

Exercises, Notebook

Nov 19 - TEST 2

Practice 10 - Nov 26-30 - Ensemble methods

Exercises Solutions

Practice 11 - Dec 3-7 - Probabilistic graphical models

Exercises, Solutions

Practice 12 - Dec 10-14 - Bayesian machine learning

Exercises, Notebook

Jan 7 - TEST 3

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