Course Description for INFO 6205: Program Structure and Algorithms
Welcome to INFO 6205: Program Structure and Algorithms, offered by the College of Engineering at Northeastern University on Coursera. This course is designed to introduce students to the fundamental principles of data structures and algorithms, focusing on their practical application and theoretical underpinnings.
Course Overview: INFO 6205 on Coursera integrates dynamic programming, graph algorithms, and greedy strategies into its curriculum, focusing on solving computational problems using Python. The course begins with dynamic memory allocation and covers data structures like lists, trees, and graphs. It then advances into complex problem-solving techniques, including dynamic programming for overlapping subproblems, graph algorithms for network flows, and greedy methods for optimization. With a hands-on approach, including programming assignments and a capstone project, the course prepares students for careers in data engineering, emphasizing both practical skills and theoretical knowledge.
Learning Objectives:
- Understand and apply various computational problem formulation techniques.
- Master algorithmic design techniques for efficient problem-solving.
- Learn proof and analysis techniques critical for evaluating algorithm correctness and efficiency.
- Transform algorithms into executable programs using Python.
- Implement, simulate, and visualize algorithms for a deeper understanding.
- Data Structures: Diving deep into the use and implementation of heaps, union-find, and more.
Course Content and Structure: The course structure includes homework assignment, exams, quizzes, and a comprehensive research project. The course examples will all use the python programming language.
Topics Covered Include:
- Introduction to algorithms
- Stable matching using the Gale-Shapley algorithm
- Algorithm analysis, including big-Oh notation
- Graphs and graph search algorithms
- Greedy algorithms for solving problems like interval scheduling and finding shortest paths
- Divide and conquer strategies, including sorting and selection algorithms
- Dynamic programming for solving complex problems with overlapping subproblems
- Network flow theories and applications
- Intractability and coping with NP-completeness
- Approximation and randomized algorithms
- Advanced data structures, including heaps and union-find
Core Curriculum:
INFO 6205 covers a comprehensive range of topics designed to provide students with a deep understanding of algorithms and data structures. The required curriculum includes:
- Introduction to Algorithms: Overview of algorithmic thinking and its importance in problem-solving.
- Stable Matching and the Gale-Shapley Algorithm: Learn about the stable matching problem and its solutions.
- Sorting and Caching: Techniques for data organization and efficient retrieval.
- Algorithm Analysis: Mastering big-Oh notation and the analysis of algorithm efficiency.
- Graphs and Graph Search Algorithms: Exploring graph representations, breadth-first search, and depth-first search.
- Greedy Algorithms: Techniques for solving optimization problems, including interval scheduling and shortest paths.
- Divide and Conquer Strategies: Fundamental approaches for sorting, selection, and beyond.
- Dynamic Programming: Solving complex problems by breaking them down into simpler subproblems.
- Network Flow: Understanding maximum flow theory and its applications.
- Intractability: Approaching NP-completeness and strategies for dealing with hard problems.
- Bayes’ Rule: Approaches for optimal task arrangement and probabilistic prediction.
- Approximation Algorithms: Techniques for finding near-optimal solutions to complex problems and leveraging randomness.
- Randomized Algorithms: Techniques for finding near-optimal solutions to complex problems and leveraging randomness.
Appendix :
Optional Topics do not count towards a course grade but are recommended for those looking for Data Engineering Careers.
- Optimal Stopping: Strategies for decision-making under uncertainty.
- Explore/Exploit: Balancing the new against the known in decision-making.
- Randomness and Networking: The role of randomness in algorithms and the basics of network theory.
- Game Theory: Understanding strategic interactions in competitive environments.
- Linear Programming: Applying linear programming to solve various computational problems.
- Fundamentals of Large Language Models (LLM): Understanding the architecture, training processes, and applications of LLMs like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).
- Ethical Considerations for Algorithms: Discussing the moral implications of AI and LLMs, including bias, fairness, transparency, and accountability in algorithmic decision-making.
- Algorithm Visualization: Leveraging AI to extract insights from large datasets, predict trends, and create dynamic visualizations.
- Reinforcement Learning: Teaching machines to make decisions by rewarding desired behaviors and outcomes in complex environments.
- Generative Models in AI: Exploring generative adversarial networks (GANs) and their applications in creating art, synthetic data generation, and more.
Methodology: Students will engage with the material through a combination of theoretical exploration and practical application. The course includes extensive use of Python for implementing, simulating, and visualizing algorithms. This hands-on approach, combined with rigorous analysis, aims to equip students with the skills necessary to tackle complex algorithmic challenges.
Target Audience: This course is intended for students and professionals with a basic understanding of programming concepts who are interested in advancing their knowledge of algorithms and data structures. Whether you're looking to improve your coding skills, prepare for technical interviews, or pursue research in computer science, INFO 6205 provides the foundational knowledge and skills you need to succeed.
By the end of this course, participants will have a comprehensive understanding of how to design, analyze, and implement algorithms effectively, with a special focus on their applications in real-world scenarios. Join us on Coursera to embark on this exciting journey into the world of algorithms and data structures at Northeastern University's College of Engineering.