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Course, academic year 2019/2020
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Selected Topics in Algorithms - NTIN101
Title in English: Selected Topics in Algorithms
Guaranteed by: Computer Science Institute of Charles University (32-IUUK)
Faculty: Faculty of Mathematics and Physics
Actual: from 2019
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: prof. RNDr. Jiří Sgall, DrSc.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: (04.05.2015)
This course covers advanced topics in theory of algorithms. Different years will be devoted to a different topic.
Aim of the course -
Last update: (04.05.2015)

Teach the students selected topics in theory of algorithms.

Literature -
Last update: (04.05.2015)

Current research papers

Syllabus -
Last update: (04.05.2015)

In the fall 2015, the course will focus on machine learning.

The field of machine learning is concerned with the automatic discovery of

regularities in data through the use of computer algorithms and with the use

of these regularities to take actions such as classifying the data into

different categories. In this course, we mostly consider the following

sub-fileds of the machine learning:

(1) Supervised learning, where we have access to a known training data

set. The training data comprises examples of the input vectors along with

their corresponding target vectors. The goal would be to train a model

using the training data in order to use the model for a test data set

later. The classification and regression problems are two known examples

of the supervised learning.

(2) Unsupervised learning, where we do not have access to a known training

data set. The goal in such unsupervised learning problems may be to

discover groups of similar examples within the data, where it is called

clustering, or to determine the distribution of data within the input

space, known as density estimation.

Here in this course we cover these two areas of learning in details. As an

example we will speak about linear regressions, PAC learning model, EM and

clustering, kernels, dimensionality reduction techniques like PCA and SVD

and learning mixture of distributions among the others. We also discuss

the new advances of these areas with respect to big data models such as

streaming and MapReduce models. This includes new sketching and sampling

techniques that have been developed very recently for supervised and

unsupervised learnings when the data is big.

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