HS2016

This course aims at providing an overview and understanding of the state of the art research in computer vision and machine learning. Each week a new paper with important results in the field is selected and made available in ILIAS. Students are required to read and understand the paper. During the seminar a list of questions is distributed and students discuss, formulate and present the answers in groups.

The main objective of this course is to introduce the students to the underlying problems when facing with natural languages ... data. This course was the following objectives: 1) to understand the problems related to representation and manipulation of text data; 2) to understand the statistical properties underlying in all text data; 3) to understand the main approaches in NLP; Practical exercises will complete the theoretical presentation. Description Introduction to Python and XML; Introduction to linguistics (morphology, syntax, semantics); simple statistical approaches (KWIC, concordances); Language models; Spelling detection and correction; Statistical models (counting words, bigrams, entropy); Parsing; Markov chains; Hidden Markov chains; Text categorization (authorship attribution); sentiment analysis; Information Retrieval; Cryptography; Text summarization. The final mark is based on both a final written exam and the results of the practical exercises. References Christopher D. Manning, Hinrich Schütze: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (MA). Pierre Nugues: An Introduction to Language Processing with Perl and Prolog. Springer, Berlin, 2006.

The main objectives of this course is to introduce the students to the various techniques and strategies that can be used to ... 1) to discover pertinent relationships (or correlations) between variables 2) to evaluate such relationships and machine learning approaches 3) to know how to conduct a machine learning process based on available data and to interpret the results. Practical exercises will complete the theoretical presentation. Description Introduction to Data Mining perspective; Association rules; Decision trees; Instance-based learning (nearest neighbors); Feature selection; Nearest Neighbor Search (Local Sensitive Hashing); Clustering; Evaluation (Train & Test, Cross-validation, Leaving-one-out); Data Streams. The final mark is based on both a final written exam and the results of the practical exercises. References Ian H. Witten, Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman, 2005. Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer, 2006. T. Hasti, R. Tibshirani, J. Friedman: The Elements of Statistical Learning. 2nd Ed. Springer, 2009. Keywords. Introduction to Data Mining perspective, Association rules, Decision trees, Instance-based learning (nearest neighbors), Clustering, Evaluation (Train & Test, Cross-validation, Leaving-one-out).

This seminar on Natural Langue Processing (NLP) will follow the following objectives: 1. Conduct your own research (on a ... limited amount of data) based on one / few scientific papers 2. Able to read / interpret scientific paper(s) 3. Go beyond a simple implementation by - Implement correctly a given algorithm - Test it - Understand its weakness - Analysis its behavior - Evaluate its performance (comparative basis, statistical tests) - and to discover the limit(s) of the proposed model Evaluation All students are working with the same corpus (data). Evaluation on two aspects: oral presentation (PowerPoint) and written report 1. Presentation 40% P1 (15%): The question to be solved P2 (25%): Your results / evaluations / analysis 2. Report 60% Question / state of the art / your solution / your analysis Your program Topics This year we will focus on text categorization problem

In this course, we first introduce some basic concepts and notions of graph theory. We then present a series of graph theoretical problems (vertex coloring, edge coloring, maximum matching, …) which have real world applications (in sports scheduling, timetabling, transmission problems, … ) and focus on how these problems may be solved. The students will also learn how to model other real world problems using the graph theoretical notions introduced. Learning outcomes: With this course, the students will get familiar with the basic notions and fundamental problems in graph theory. They will learn how to use these theoretical problems to model real world problems as well as how to solve them.

https://diuf.unifr.ch/xi/ds/