Pennsylvania State University 3 credits A-I
This course introduces students to essential algorithm design and analysis techniques with a focus on application in data science and machine learning. The course introduces students to elementary and advanced data structures to store and manipulate data. The course teaches different algorithm design and analysis techniques including classical paradigms such as divide and conquer, greedy, and dynamic programming, and advanced techniques such as randomized methods and algorithm tailored to machine learning such as gradient descent and online decision making. The course explores these techniques in context of different data types used in machine learning such as matrices, graphs and sequences. Students' learning of these concepts, techniques, and methods will be facilitated by exemplary programming demonstrations and coding exercises so that the students can utilize these algorithmic solutions in practical applications.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.