If you've ever worked with the sliding window technique and felt like the "fixed-size" window explanations were just scratching the surface, you're not alone — and you're exactly who this course is for.

Beyond Fixed Windows: Adaptive Sliding Algorithms is the first comprehensive course to focus specifically on the inner mechanics of adaptive sliding windows and pointer manipulation in iterative structures. While most resources only teach the basic fixed-size window template, this course takes you deeper — exploring how dynamic, condition-driven windows work in real coding interviews and algorithm design.

What You'll Learn

• The fundamentals of adaptive (dynamic-size) sliding window problems

• How to manage window boundaries based on conditions (e.g. distinct elements, sum constraints, frequency maps)

• Mastery of two-pointer approaches that don’t involve explicit windowing

• Real-world patterns like:

  • Longest/Shortest subarray that meets a condition

  • Substring problems with at most or exactly K distinct elements

  • Minimum window problems (e.g. "smallest substring containing a pattern")

  • Counting and optimization strategies using prefix sums and hash maps

• In-place operations and performance optimization techniques

• Debugging adaptive window logic through visual walkthroughs and print tracing

• How interviewers expect you to adapt base patterns to new constraints

Why This Course Is Unique

This is not a survey course that glosses over templates. Instead, you'll dive deeply into how adaptive windows actually work, why they break, how to fix them, and how to apply the logic to new problems. No other course provides such an intensive breakdown of pointer-driven loop behavior in the context of algorithmic problem solving.

By the end of the course, you won’t just recognize the patterns — you'll be able to build your own adaptive solutions from scratch with full control over correctness, efficiency, and clarity.