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Algorithm Design And Analysis - MIT OCW
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	L-1:	Overview, Interval Scheduling

	L-2:	Divide & Conquer: Convex Hull, Median Finding

	L-3:	Divide & Conquer: FFT

	L-4:	Divide & Conquer: Van Emde Boas Trees

	L-5:	Amortization: Amortized Analysis

	L-6:	Randomization: Matrix Multiply, Quicksort

	L-7:	Randomization: Skip Lists

	L-8:	Randomization: Universal & Perfect Hashing

	L-9:	Augmentation: Range Trees

	L-10:	Dynamic Programming: Advanced DP

	L-11:	Dynamic Programming: All-pairs Shortest Paths

	L-12:	Greedy Algorithms: Minimum Spanning Tree

	L-13:	Incremental Improvement: Max Flow, Min Cut

	L-14:	Incremental Improvement: Matching and Baseball Elimination

	L-15:	Linear Programming: LP, Reductions, Simplex

	L-16:	Complexity: P, NP, NP-completeness, Reductions

	L-17:	Complexity: Approximation Algorithms 

	L-18:	Complexity: Fixed-parameter Algorithms

	L-19:	Synchronous Distributed Algorithms: Symmetry-breaking. Shortest-paths Spanning Trees

	L-20:	Asynchronous Distributed Algorithms: Shortest-paths Spanning Trees

	L-21:	Cryptography: Hash Functions

	L-22:	Cryptography: Encryption

	L-23:	Cache-oblivious Algorithms: Medians & Matrices

	L-24:	Cache-oblivious Algorithms: Searching & Sorting








Advanced Algorithms - MIT OCW
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	L-1:	Fibonacci Heaps

	L-2:	MST: Persistent Data Structures

	L-3:	Splay Trees-I

	L-4:	Splay Trees-II, Suffix Trees-I, Tries

	L-5:	Suffix Trees-II, Tries, Dial's Algorithm

	L-6:	Dijkstra's Algorithm, Van Emde Boas Queues-I

	L-7:	Van Emde Boas Queues-II, Hashing

	L-8:	2-Level Hashing, Network Flows

	L-9:	Network Flows: Augmenting Paths, Maximum 
	        Augmenting Paths, Scaling	 	 

	L-10:	Reductions between Flow Problems, Bipartite Matching, 
	        Shortest Augmenting Path, Blocking Flows-I

	L-11:	Blocking Flows-II

	L-12:	Min-Cost Flow Algorithms-I

	L-13:	Min-Cost Flow Algorithms-II, Linear Programming-I

	L-14:	Linear Programming-II, Structure of Optima, Weak Duality

	L-15:	Linear Programming-III, Strong Duality

	L-16:	Linear Programming, Complementary Slackness, 
	        Algorithms: Simplex, Ellipsoid
	L-17:	Linear Programming, Algorithms: Interior Point		 	 

	L-18:	Approximation Algorithms, NP-hard problems	 	

	L-19:	4/3-Approximation for TSP	 	 

	L-20:	Relaxations, Directed TSP	 	 

	L-21:	Randomized Rounding, Chernoff Bound, Fixed Parameter 
	        Tractability, Kernelization	

	L-22:	Online Algorithms ie. Ski Rental, Load Balancing, Paging

	L-23:	Randomized Online Algorithms (Adversaries, Fiat's Marking Algorithm, 
	        Potential Functions, Yao's Minimax Principle)	

	L-24:	K-Server Problem, Double-Coverage Algorithm, Computational 
	        Geometry Introduction (Orthogonal Range Search)	

	L-25:	Sweep Algorithms (Convex Hull, Segment Intersection, Voronoi Diagrams)	

	L-26:	Sweep Algorithms (Voronoi Diagrams), Randomized Incremental 
	        Constructions, Backwards Analysis, Linear Programming in 
	        Fixed Dimension	

	L-27:	External Memory Algorithms	

	L-28:	Cache Oblivious Algorithms: Matrix Multiplication, Linked Lists, Median

	L-29:	Cache Oblivious Algorithms: Search Streaming Model

	L-30:	Parallel Algorithms



Advanced Data Structures - MIT OCW
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	L-1: Persistent Data Structures

	L-2: Retroactive Data Structures

	L-3: Geometric Structures-I

	L-4: Geometric Structures-II

	L-5: Dynamic Optimality-I

	L-6: Dynamic Optimality-II

	L-7: Memory Hierarchy Models

	L-8: Cache-Oblivious Structures-I

	L-9: Cache-Oblivious Structures-II

	L-10: Dictionaries

	L-11: Integer Models

	L-12: Fusion Trees

	L-13: Integer Lower Bounds

	L-14: Sorting in Linear Time

	L-15: Static Trees

	L-16: Strings

	L-17: Succinct Structures-I

	L-18: Succinct Structures-II

	L-19: Dynamic Graphs-I

	L-20: Dynamic Graphs-II

	L-21: Dynamic Connectivity Lower Bound

	L-22: History of Memory Models







Distributed Algorithms - MIT OCW
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	L-1:	Course overview. Synchronous networks. Leader 
	        election in synchronous ring networks

	L-2:	Leader election in rings. Basic computational tasks in 
	        general synchronous networks: leader election. Breadth-first search. 
	        Broadcast and convergecast. Shortest paths

	L-3:	Spanning trees. Minimum spanning trees

	L-4:	Fault-tolerant consensus. Link failures: the two generals problem. Process failures (stopping, Byzantine). Algorithms for agreement with stopping and Byzantine failures. Exponential information gathering

	L-5:	Number-of-processor bounds for Byzantine agreement. Weak Byzantine agreement. Time bounds for consensus problems

	L-6:	k-set-agreement. Approximate agreement. Distributed commit

	L-7:	Asynchronous distributed computing. Formal modeling of asynchronous systems using interacting state machines (I/O automata). Proving correctness of distributed algorithms.	

	L-8:	Non-fault-tolerant algorithms for asynchronous networks. Leader election, breadth-first search, shortest paths, broadcast and convergecast.	

	L-9:	Spanning trees. Gallager et al. minimum spanning trees.	

	L-10:	Synchronizers. Synchronizer applications. Synchronous vs. asynchronous distributed systems.	

	L-11:	Time, clocks, and the ordering of events. State-machine simulation. Vector timestamps.	

	L-12:	Stable property detection. Distributed termination. Global snapshots. Deadlock detection.	

	L-13:	Asynchronous shared-memory systems. The mutual exclusion problem. Mutual exclusion algorithms.	
	L-14:	More mutual exclusion algorithms. Bounds on shared memory for mutual exclusion. Resource allocation. The Dining Philosophers problem.	

	L-15:	Shared-memory multiprocessors. Contention, caching, locality. Practical mutual exclusion algorithms. Reading/writing locks.	

	L-16:	Impossibility of consensus in asynchronous, fault-prone, shared-memory systems.	

	L-17:	Atomic objects	

	L-18:	Atomic snapshot algorithms. Atomic read/write register algorithms.	

	L-19:	List algorithms: locking algorithms, optimistic algorithms, lock-free algorithms, lazy 
	algorithms.	

	L-20:	Transactional memory: obstruction-free and lock-based implementations.	

	L-21:	Wait-free computability. The wait-free consensus hierarchy.	

	L-22:	Wait-free vs. f-fault-tolerant atomic objects. Boosting fault-tolerance.	

	L-23:	Asynchronous network model vs. asynchronous shared-memory model. Impossibility of consensus in asynchronous networks. Failure detectors and consensus. Paxos consensus algorithm.	

	L-24:	Self-stabilizing algorithms	

	L-25:	Timing-based systems. Modeling and verification. Timing-based algorithms for mutual exclusion and consensus. Clock synchronization.	






Data Structures in Java - Java Specialists (By Dr. Heinz Kabutz)



Udemy 
—————

Complexity Theory Basics - Holczer Balazs/Udemy
Data Structures In Java I - Holczer Balazs/Udemy

Advanced Algorithms In Java I - Holczer Balazs/Udemy
Algorithms And Data Structures In Java II - Holczer Balazs/Udemy


Byte-sized-chunks: Stacks, Queues, Binary Trees, Heaps - Udemy 
Byte-sized-chunks: Graph Algorithms And Problems In Java - Udemy 




Udacity
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Intro To Algorithms - Udacity 
Computability, Complexity And Algorithms - Udacity 





Algorithms: Design And Analysis - Stanford University/ Coursera
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Algorithms: Design And Analysis I - Stanford University
Algorithms: Design And Analysis Ii - Stanford University
Convex Optimization - Stanford University




Algorithms - Princeton University/Coursera
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Algorithms Part I - Princeton University/Coursera
Algorithms Part II - Princeton University/Coursera
Analysis Of Algorithms - Princeton University/Coursera


Approximation Algorithms - Ecole Normale Superieure/ Coursera
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Approximation Algorithms Part I (Ecole Normale Superieure) - Coursera
Approximation Algorithms Part Ii (Ecole Normale Superieure) - Coursera
Statistical Mechanics: Algorithms And Computations (Ecole Normale Superieure) - Coursera



Data Structures And Performance - University Of California, San Diego/ Coursera 

Advanced Modeling For Discrete Optimization - university Of Melbourne/ Coursera





Algorithms Specialization - Stanford University/ Coursera
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Divide And Conquer, Sorting And Searching And Randomizing Algorithms (Stanford University) - Coursera

Graph Search, Shortest Paths, And Data Structures (Stanford University) - Coursera

Greedy Algorithms, Minimum Spanning Trees, And Dynamic Programming (Stanford University) - Coursera 

Shortest Paths Revisited, Np-complete Problems And What To Do About Them (Stanford University) - Coursera







Data Structures and Algorithms Specialization - UC San Diego/ Coursera
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Algorithmic Toolbox (University Of California, San Diego) - Coursera
Data Structures (University Of California, San Diego) - Coursera
Algorithms On Graphs (University Of California, San Diego) - Coursera
Algorithms On Strings (University Of California, San Diego) - Coursera
Advanced Algorithms And Complexity (University Of California, San Diego) - Coursera
Genome Assembly Programming Challenge (University Of California, San Diego) - Coursera








Top Algorithms And Data Structures For Competitive Programming - Geeks For Geeks (GfG)
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	I. Graph algorithms
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			Breadth First Search (BFS)
			Depth First Search (DFS)
			Shortest Path from source to all vertices - Dijkstra
			Shortest Path from every vertex to every other vertex - Floyd Warshall
			Minimum Spanning tree - Prim
			Minimum Spanning tree - Kruskal
			Topological Sort
			Johnson’s algorithm
			Articulation Points (or Cut Vertices) in a Graph
			Bridges in a graph

	II. Dynamic programming
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			Longest Common Subsequence
			Longest Increasing Subsequence
			Edit Distance
			Minimum Partition
			Ways to Cover a Distance
			Longest Path In Matrix
			Subset Sum Problem
			Optimal Strategy for a Game
			0-1 Knapsack Problem
			Assembly Line Scheduling


	III. Searching and Sorting
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			Binary Search
			Quick Sort
			Merge Sort
			Order Statistics
			KMP algorithm
			Rabin karp
			Z’s algorithm
			Aho Corasick String Matching
			Counting Sort
			Manacher’s algorithm


	IV. Number theory and Other Mathematical
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			Primality Test - Introduction and School Method
			Primality Test - Fermat Method)
			Primality Test - Miller–Rabin
			Sieve of Eratosthenes
			Segmented Sieve
			Wilson’s Theorem
			Prime Factorisation
			Pollard’s rho algorithm


	V. Geometrical and Network Flow Algorithms
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			Convex Hull
			Graham Scan
			Line Intersection
			Interval Tree
			Matrix Exponentiation and this
			Maxflow Ford Furkerson Algo and Edmond Karp Implementation
			Min cut
			Stable Marriage Problem
			Hopcroft–Karp Algorithm for Maximum Matching
			Dinic’s algo and e-maxx


	VI. Modulo Arithmetic Algorithms		
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			Basic and Extended Euclidean algorithms
			Euler’s Totient Function
			Modular Exponentiation
			Modular Multiplicative Inverse
			Chinese remainder theorem Introduction
			Chinese remainder theorem and Modulo Inverse Implementation
			nCr%m 


	VII. Data Structures
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			Binary Indexed Tree or Fenwick tree
			Segment Tree (RMQ, Range Sum and Lazy Propagation)
			K-D tree (See insert, minimum and delete)
			Union Find Disjoint Set (Cycle Detection and By Rank and Path Compression)
			Tries
			Suffix array
			Sparse table
			Suffix automata
			Suffix automata II
			LCA and RMQ