Kuzmiakova Computer Science, Algorithms and Complexity

Erscheinungsjahr 2020
ISBN: 978-1-77407-952-2
Verlag: Arcler Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 272 Seiten, Electronic book text, Format (B × H): 152 mm x 229 mm

The book defines complexity as a numerical function T (n)-the relationship between time and input size n, as one of the basic ideas of computer science. The computational complexity is categorized by algorithm based on its nature and function. The (computational) complexity of the algorithm is a measurement of the ratio of computational resources (time and space) consumed when a particular algorithm is running. For these issues, the book tries to locate heuristic algorithms which can almost explain the problem and operate in a reasonable timeframe. Different kinds of algorithms are described such as graph and network algorithms, algebraic algorithms, parallel algorithms and randomized algorithms.

Autoren/Hrsg.

Kuzmiakova, Adele

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

- Chapter 1: Basic Techniques for Design and Analysis of Algorithms
- Chapter 2: Computational Complexity Theory
- Chapter 3: Graph and Network Algorithms
- Chapter 4: Cryptography
- Chapter 5: Algebraic Algorithms
- Chapter 6: Parallel Algorithms
- Chapter 7: Randomized Algorithms
- Chapter 8: Pattern Matching and Text Compression Algorithms
- Chapter 9: Genetic Algorithms
- Chapter 10: Combinational Optimization

Über Autor(innen)

Adele Kuzmiakova is a computational engineer focusing on solving problems in machine learning, deep learning, and computer vision. Adele attended Cornell University in New York, United States for her undergraduate studies. She studied engineering with a focus on applied math. While at Cornell, she developed close relationships with professors, which enabled her to get involved in academic research to get hands-on experience with solving computational problems. She was also selected to be Accel Roundtable on Entrepreneurship Education (REE) Fellow at Stanford University and spent 3 months working on entrepreneurship projects to get a taste of entrepreneurship and high-growth ventures in engineering and life sciences. The program culminated in giving a presentation on the startup technology and was judged by Stanford faculty and entrepreneurship experts in Silicon Valley. After graduating from Cornell, Adele worked as a data scientist at Swiss Federal Institute of Technology in Lausanne, Switzerland where she focused on developing algorithms and graphical models to analyze chemical pathways in the atmosphere. Adele also pursued graduate studies at Stanford University in the United States where she entered as a recipient of American Association of University Women International Fellowship. The Fellowship enabled her to focus on tackling important research problems in machine learning and computer vision. Some research problems she worked on at Stanford include detecting air pollution from outdoor public webcam images. Specifically, she modified and set up a variety of pre-trained architectures, such as DehazeNet, VGG, and ResNet, on public webcam images to evaluate their ability to predict air quality based on the degree of haze on pictures. Other deep learning problems Adele worked on include investigating the promise of second-order optimizers in deep learning and using neural networks to predict sequences of data in energy consumption. Adele also places an emphasis on continual education and served as a Student Leader in PyTorch scholarship challenge organized by Udacity. Her roles as the Student Leader were helping students debug their code to train neural networks with PyTorch and providing mentorship on technical and career aspects. Her hobbies include skiing, playing tennis, cooking, and meeting new people.