1. identify and understand why a problem belongs to the Big Data category
2. apply the MapReduce programming model when encountering certain types of problems
3. design and evaluate a system for finding similar entities in a large data set
4. design and evaluate a system for finding frequent sets in a large data set
5. design and evaluate a node ranking system for a very large data set represented by a graph
6. design and evaluate a recommendation system
7. apply appropriate algorithms to find groups in a large set of falls
8. apply appropriate algorithms to process data flows

Lecturer-driven classroom presentations of theoretical concepts.

Examples and problem solving during lectures.

Software implementation of selected massive dataset analysis methods. Students individually implement given assignment in a recommended programming language or tool, and submit their solutions to automatic online evaluation.

1. Locality-sensitive hashing (LSH), minhash and simhash algorithms
2. Locality-sensitive hashing (LSH), minhash and simhash algorithms
3. Graph mining
4. Web search (PageRank and HITS)
5. Data mining with Map-Reduce, Feature selection (filter methods, subset selection, wrapper method)
6. Data stream mining
7. Data stream mining
8. Midterm exam
9. Time series and sequences mining
10. Collaborative filtering and recommender engines
11. Clustering algorithms for large datasets (BFR, CURE)
12. Sampling, filtering and estimating data stream moments
13. Large-scale algorithms for mining frequent item sets (Apriori, PCY, SON)
14. Detecting communities in large graphs (Girvan-Newman, Affiliation-Graph Model)
15. Final exam