CloseHelpPrint
Kies de Nederlandse taal
Course module: 880083-M-6
880083-M-6
Machine Learning
Course info
Course module880083-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
M Communication and Information Sciences
Convenant TSH
Contact persondr. G.A. Chrupala
Lecturer(s)
Coordinator course
dr. G.A. Chrupala
Other course modules lecturer
Contact person for the course
dr. G.A. Chrupala
Other course modules lecturer
Starting block
BLOK 2/  BLOK 4
Course mode
Full-time
Remarks-
Registration openfrom 12/10/2018 09:00 up to and including 25/01/2019
Aims
Please Mind: you can still register for the course, by only enroling in the 'lecture' mode. You will be assigned a practicum group after the first lecture. 

1. Understand the workflow of a machine-learning project
2. Understand the mathematical and algorithmic formulation of learners such as Decision Trees, Perceptron, Logistic Regression and Neural Networks
3. Implement them and apply them to standard datasets using the Python programming language 
4. Use Python libraries  as numpy and scikit-learn to
   a. Extract features from examples
   b. Train classification and regression models
   c. Evaluate models on new data
5. Apply classification and regression learning models on realistic datasets
6. Perform evaluation and carry out error analysis of machine learning experiments

Evaluation

Take-home assignment (40%)  
Final exam (60%)
 
Recommended Prerequisites

Data Processing Advanced is strongly recommended as a companion course


Required Prerequisites

Research Skills: Data Processing or Seminar data processing

Familiarity with Python at a basic level is a must.
Content
Learning from examples is one of the most basic aspects of human intelligence. The field of Machine Learning tries to replicate this skill and apply it to real-world problems: filtering spam from your mailbox, recognizing faces in photos, classifying documents, recommending movies and songs, or detecting credit-card fraud.

In this course you will learn the theory behind classic machine learning algorithms and at the same time get hands-on experience with using the programming language Python to apply machine learning methods to practical problems.

You will learn how to implement a typical workflow of a machine-learning project: start by analyzing your data and extracting features from your examples, then train a classification or regression model on the data, and finally evaluate how well the model makes predictions on new data.

You will learn the mechanics behind very simple learners such as the Perceptron, and learn how to implement them in Python. You will also become familiar with the Python library scikit-learn which provides easy-to-use implementations of many different classifiers and learning algorithms.

Finally, you will have the opportunity to apply machine learning techniques to realistic large data sets.

The course will consists of lectures as well as practical exercise sessions where students work on machine learning assignments.
 
 
Timetable information
880083-M-6/Machine Learning
Required materials
-
Recommended materials
Literature
-
Title:A course in Machine Learning
Author:Hal Daumé III
Literature
-
Title:Python Machine Learning
Author:Sebastian Raschka
Literature
http://scikit-learn.org/stable/tutorial/basic/tutorial.html#introduction
Title:An introduction to machine learning with scikit-learn
CloseHelpPrint
Kies de Nederlandse taal