- [ home ]
- [ courses ]
- [ distance delivery ]
- [ degrees ]
- [ certificates ]
- [ enrollment + tuition ]
- [ resources ]
[ course login ]
Algorithms are the heart of computer science, and their essential nature is to automate some aspect of the collecting, organizing and processing of information. Today, information of all kinds is increasingly available in enormous quantities. However, our ability to make sense of all this information, to manage, organize and search it, and to use it for practical purposes, e.g., self-driving cars, adaptive computation, search algorithms for the Internet or for social networks, artificial intelligence, and many scientific applications, relies on the design of efficient algorithms, that is, algorithms that are fast, use little memory and provide guarantees on their performance.
This graduate-level course will cover topics related to algorithm design and analysis. Topics will include divide and conquer algorithms, greedy algorithms, graph algorithms, algorithms for social networks, computational biology, optimization algorithms, randomization and algorithm analysis. We will not cover any of these topics exhaustively. Rather, the focus will be on algorithmic thinking, performance guarantees and boundary cases, efficient solutions to practical problems and understanding how to analyze algorithms. Advanced topics will cover a selection of modern algorithms, many of which come from real-world applications.
Course Work and Grading:
See the syllabus for description of the required coursework, including descriptions of the problem sets, crowd-source lecture and independent project assignments.
5454-001 (or by permission): Grades will be assigned based on attendance (0.2), crowd-source lecture or independent project (0.3), and problem sets (0.5).
5454-740: Grades will be assigned based on the project (0.4) and problem sets (0.6).
Undergraduate algorithms (CSCI 3104), data structures (CSCI 2270), discrete mathematics (CSCI 2824) and two semesters of calculus, or equivalents. This class assumes familiarity with asymptotic analysis (Big-O, etc.), recurrence relations and the correct implementation of basic algorithms. Students without the required background may struggle to keep up with the lectures and assignments. There will be a brief survey at the beginning of the semester to help me get an idea of the class's preparation.
If a proctor is indicated as “required” above, you will need an appropriate person to proctor exams/quizzes for the course. Examples of an appropriate person to proctor your exams/quizzes are your supervisor/manager, an education/training or personnel official in your company, or a librarian. The proctor may not be a friend, relative, or co-worker. The proctor’s address must be a business address. More detailed information about proctors is available on our Exam/Quiz Proctor page.
For those able to come to campus, CAETE provides free proctoring services.
Contact us at 303-492-6331 or email@example.com to schedule a test appointment or if you have questions.
Meeting Days Legend: Monday (M), Tuesday (T), Wednesday (W), Thursday (R), Friday (F), Saturday (S), Sunday (U)
Summer Terms: M = Maymester, A = 1st 5 weeks, B= 2nd 5 weeks, C = 8 weeks, D= 10 weeks
Refer to the Academic Calendar for specific dates.
|Spring 2013||01:00 PM - 02:15 PM||MW||ECCS 1B12||Clauset, A|
|Spring 2012||04:00 PM - 05:15 PM||MW||ECCS 1B12||Clauset, A|
|Spring 2008||04:00 PM - 05:15 PM||MW||ECCS 1B12||Gabow, H|
|Spring 2007||04:00 PM - 05:15 PM||MW||ECCS 1B12||Gabow, H|
|Spring 2006||04:00 PM - 05:15 PM||MW||ECCS 1B12||Gabow, H|