Advanced Algorithm Design and Analysis
Master advanced algorithmic concepts including greedy algorithms, dynamic programming, and NP-completeness.
Master advanced algorithmic concepts including greedy algorithms, dynamic programming, and NP-completeness.
This comprehensive course explores advanced concepts in algorithm design and analysis, building upon fundamental programming knowledge. Students will dive deep into greedy algorithms for scheduling and clustering, dynamic programming techniques for optimization problems, and the theoretical foundations of NP-completeness. The curriculum combines rigorous theoretical concepts with practical implementation through programming assignments. Focusing on conceptual understanding over low-level details, the course provides mastery of algorithmic thinking comparable to graduate-level education. Students will implement algorithms in their preferred programming language and complete multiple-choice assessments to reinforce learning.
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What you'll learn
Master advanced greedy algorithms for scheduling and clustering problems
Implement dynamic programming solutions for optimization challenges
Understand NP-completeness and its implications for algorithm design
Analyze heuristics and local search techniques
Develop practical programming skills through hands-on algorithm implementation
Skills you'll gain
This course includes:
PreRecorded video
6 programming assignments, 6 problem sets, final exam
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Limited Access access
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Module Description
This advanced algorithms course covers sophisticated techniques in algorithm design and analysis. The curriculum includes comprehensive coverage of greedy algorithms, dynamic programming, and NP-completeness. Students learn through a combination of theoretical instruction and practical programming assignments. The course emphasizes conceptual understanding while providing hands-on experience with implementing various algorithms. Topics include scheduling, minimum spanning trees, clustering, Huffman codes, knapsack problems, and sequence alignment.
Fee Structure
Instructors
1 Course
A Distinguished Medical Leader in Healthcare Innovation and Education
David Svec serves as Clinical Associate Professor at Stanford School of Medicine and Chief Medical Officer of Stanford Health Care Tri-Valley, where he combines clinical excellence with administrative leadership. After earning his joint MD/MBA from Case Western Reserve University and completing his residency at Stanford in 2012, he has built an impressive career spanning clinical care, medical education, and healthcare administration. His contributions to medical education have earned him multiple teaching awards, including the David A. Rytand Clinical Teaching Award, the Arthur L. Bloomfield Award, and the Lawrence Mathers Award. Beyond his clinical work, Svec champions sustainability initiatives at Stanford Health Care, co-leads the Stanford Health Consulting Group class, and focuses on high-value care through quality improvement projects. His leadership extends to numerous committees and initiatives, including serving as Medical Director of the Hospitalist Team and participating in strategic planning for Stanford Health Care Tri-Valley. His research interests focus on healthcare efficiency, quality improvement, and delivering high-value care, while maintaining active involvement in teaching and mentoring the next generation of medical professionals.
1 Course
A Healthcare Innovation Leader and Medical Educator
Alistair Aaronson serves as Clinical Assistant Professor at Stanford School of Medicine, where he has made significant contributions to surgical co-management and interprofessional healthcare education. After earning his B.S. in Molecular Biology from Johns Hopkins University and M.D. from the Medical University of South Carolina, followed by residency at Cedars-Sinai Medical Center, he joined Stanford's hospitalist program in 2013. As a surgical co-management hospitalist, he specializes in caring for complex orthopedic surgery patients with multiple medical comorbidities, focusing on reducing post-operative complications and optimizing clinical outcomes. His work extends beyond clinical care through his involvement in patient safety and quality improvement committees, while his research focuses on care coordination, patient engagement, and technological optimization of clinical care. He has contributed significantly to interprofessional education at Stanford, helping develop curriculum that bridges gaps between different healthcare disciplines and promotes collaborative patient care.
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