# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

### Information in the Degree Programme Tables may still be subject to change in response to Covid-19

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# Undergraduate Course: Algorithmic Foundations of Data Science (INFR11156)

 School School of Informatics College College of Science and Engineering Credit level (Normal year taken) SCQF Level 11 (Year 4 Undergraduate) Availability Available to all students SCQF Credits 10 ECTS Credits 5 Summary The course aims to introduce algorithmic techniques that form the foundations of processing and analysing massive datasets of various forms. In particular, the course discusses how to pre-process massive datasets, efficiently store massive datasets, design fast algorithms for massive datasets, and analyse the performance of designed algorithms. Through various examples and the coursework, the students will see applications of the topics discussed in class in other areas of computer science, e.g., machine learning, and network science. Course description The course is to discuss algorithmic techniques that form the foundations of processing and analysing massive datasets of various forms. Specific techniques covered in the course include effective representation of datasets, extracting useful information from a dataset based on algebraic tools, designing faster algorithms based on sampling and sketching techniques. Students in class will learn these techniques through intuitions, theoretical reasoning, and practical examples. The syllabus includes: High-dimensional spaces Best-fit subspaces and singular value decomposition Spectral algorithms for massive datasets Data streaming algorithms Clustering Graph sparsification
 Pre-requisites It is RECOMMENDED that students have passed Algorithms and Data Structures (INFR10052) Co-requisites Prohibited Combinations Other requirements This course has the following mathematics prerequisites: 1 Calculus: limits, sums, integration, differentiation, recurrence relations 2 Graph theory: graphs, digraphs, trees 3 Probability: random variables, expectation, variance, Markov's inequality, Chebychev's inequality 4 Linear algebra: vectors, matrices, eigenvectors and eigenvalues, rank 5 Students should be familiar with the definition and use of big-O notation, and must be comfortable both reading and constructing mathematical proofs using various methods such as proof by induction and proof by contradiction.
 Pre-requisites This course has the following mathematics prerequisites: 1 Calculus: limits, sums, integration, differentiation, recurrence relations 2 Graph theory: graphs, digraphs, trees 3 Probability: random variables, expectation, variance, Markov's inequality, Chebychev's inequality 4 Linear algebra: vectors, matrices, eigenvectors and eigenvalues, rank 5 Students should be familiar with the definition and use of big-O notation, and must be comfortable both reading and constructing mathematical proofs using various methods such as proof by induction and proof by contradiction. High Demand Course? Yes
 Not being delivered
 On completion of this course, the student will be able to: Demonstrate familiarity with fundamentals for processing massive datasets.Describe and compare the various algorithmic design techniques covered in the syllabus to process massive datasets.Apply the learned techniques to design efficient algorithms for massive datasets.Apply basic knowledge in linear algebra and probability theory to prove the efficiency of the designed algorithm.Use an appropriate software to solve certain algorithmic problems for a given dataset.
 The main textbook for the course is: Avrim Blum, John Hopcroft, and Ravindran Kannan: Foundations of Data Science. https://www.cs.cornell.edu/jeh/book.pdf
 Graduate Attributes and Skills As the outcome of the course, a student should be able to apply the learned mathematical knowledge to analyse and process massive datasets, and use these tools to solve algorithmic problems occurring in practice. Keywords Machine Learning,Computer Science,Artificial Intelligence,Theoretical Computer Science
 Course organiser Dr He Sun Tel: (0131 6)51 5622 Email: H.Sun@ed.ac.uk Course secretary Miss Clara Fraser Tel: (0131 6)51 4164 Email: clara.fraser@ed.ac.uk
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