Courses tagged with "Basic Genetics" (53)

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

The level of popularity you experienced in childhood and adolescence is still affecting you today in ways that you may not even realize. Learn about how psychologists study popularity and how these same concepts can be used in adulthood to be more successful at work, become better parents, and have a happier life.

This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet.

Lernen Sie die wichtigsten sprachtechnologischen Methoden kennen, um Texte mit digitalen Werkzeugen zu erschliessen!

Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.

Search engines are essential tools for managing and mining big text data. Learn how search engines work, the major search algorithms, and how to optimize search accuracy.

This course is about building 'web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm. In the past, this course has been offered at the Indian Institute of Technology Delhi as well as the Indraprastha Institute of Information Technology Delhi.

This course provides a brief introduction to game theory. Our main goal is to understand the basic ideas behind the key concepts in game theory, such as equilibrium, rationality, and cooperation. A number of applications in economics, politics, and biology will be discussed.

Infrastructure, Algorithms, and Visualizations

本課程有兩大課程目標： 1. 使同學了解如何以搜尋達成人工智慧 2. 使同學能將相關技術應用到自己的問題上

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]