Machine Learning
Full course description
Section titled “Full course description”Machine learning introduces the student to a broad area of artificial intelligence that aims at developing computer systems that automatically improve their performance with experience. Machine learning algorithms are widely employed and are encountered daily. Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice. The course will present both the basic, and the state-of-the-art techniques of machine learning. The practical use of the presented techniques and the problems of developing real machine-learning applications will be emphasized. The course is accompanied by practical labs that help the student understand the working of machine learning algorithms. After completing this course students will be able to use machine learning algorithms when encountered with a learning problem. Additionally students will be able to judge the quality of the model that is learned.
Prerequisites
Section titled “Prerequisites”Students entering the course should have a working knowledge of probability theory/statistics, logic and algorithms (including programming).
Recommended reading
Section titled “Recommended reading”- I.H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, ISBN 0-12-088407-0
- T. Mitchell (1997). Machine Learning, McGraw-Hill, ISBN 0-07-042807-7.
Useful Links to Guides/Blogs/Online resources
The following links are to outside sources that do not belong to MSV Incognito or DKE, however students may find these very useful to supplement their studies as the Machine Learning course is very fast paced with a lot of material to learn. If you find a website/blog/article that you think may help students, please send a link to edukatie@msvincognito.nl.
- http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture10.html
- https://www.ibm.com/developerworks/library/os-weka1/index.html (Fantastic introduction to Weka and Data Mining)
- https://www.ibm.com/developerworks/library/os-weka2/ (A follow up with more information on Classification Modelling)
- https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
- Wikipedia pages on relevant topics
- https://en.wikipedia.org/wiki/Overfitting
- https://en.wikipedia.org/wiki/Machine_learning
- https://en.wikipedia.org/wiki/Data_mining
- https://en.wikipedia.org/wiki/Statistical_classification
- https://en.wikipedia.org/wiki/K-means_clustering
- https://en.wikipedia.org/wiki/Q-learning
- https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm
- https://en.wikipedia.org/wiki/K-d_tree
- https://www.udacity.com/course/machine-learning–ud262 (free online course in Machine Learning given by Georgia Tech, a top tech institute in the USA)
- http://cs229.stanford.edu/notes/cs229-notes1.pdf (notes for the Stanford course in Machine Learning, given by Andrew Ng)
- https://itnext.io/reinforcement-learning-with-q-tables-5f11168862c8 (reinforcement learning with q tables)
- Visualizing K-Means Clustering
The second part of the course is heavily based on Andrew Ng’s course on Machine Learning. The Coursera videos can be found here.
Summary
Section titled “Summary”A summary is provided for this course written by one of our students. Feel free to keep it up to date and make adaptations by editing the latex file. It is appreciated to upload your edits and share them amongst the students. Also feel free to add your name on the author list if you made some edits!
Source: Previous wiki page