Design and Analysis of Algorithms
Learn algorithm design and analysis, covering sorting, graphs, dynamic programming, and complexity theory. Gain practical skills in efficient problem-solving and algorithm implementation.
Advanced Algorithms - COMPSCI 224
Explore cutting-edge algorithmic techniques through comprehensive lectures, enhancing problem-solving skills and deepening understanding of complex computational challenges.
Design and Analysis of Algorithms
Comprehensive exploration of algorithm design and analysis, covering complexity, sorting, data structures, and optimization techniques for efficient problem-solving.
Introduction to Algorithms and Analysis
Comprehensive exploration of algorithmic concepts, from sorting and analysis to advanced data structures and graph algorithms, emphasizing practical implementation and theoretical foundations.
Design & Analysis of Algorithms
Comprehensive exploration of algorithm design techniques, analysis frameworks, and problem-solving strategies, covering divide-and-conquer, greedy algorithms, dynamic programming, and NP-completeness.
Machine Learning for Genomics Fall 2020
Fall 2020 Prof. Manolis KellisComputational Biology: Genomes, Networks, Evolution, HealthMachine Learning in Genomics: Dissecting the circuitry of Human Disease
MIT Computational Biology - Genomes, Networks, Evolution, Health - Fall 2018
Comprehensive exploration of computational biology, covering genomics, networks, evolution, and health applications through advanced algorithms, data analysis, and cutting-edge research topics.
Machine Learning in Genomics - Fall 2019
Comprehensive exploration of computational biology techniques, from dynamic programming to deep learning, applied to genomics, epigenomics, and systems biology. Covers cutting-edge topics like single-cell genomics and genome engineering.
Design and Analysis of Algorithms
Comprehensive exploration of algorithm design techniques, analysis frameworks, and problem-solving strategies, covering divide-and-conquer, greedy algorithms, dynamic programming, and NP-completeness.
Reinforcement Learning Course
Comprehensive exploration of reinforcement learning concepts, from basics to advanced topics, including deep RL, policy gradients, and practical applications in classic games.
Computer Algorithms - 2
The course discusses efficient algorithms from a large number of domains. This course assumes the knowledge of data structures, the knowledge big-O notation and the concept of time and space complexity of an algorithm.
Fundamental Algorithms - Design and Analysis
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems.
Optimal Binary Search Tree Using Dynamic Programming - Successful Searches Only
Explore optimal binary search tree construction using dynamic programming for successful searches. Learn to minimize expected search time based on access frequencies.
Optimal Binary Search Tree Using Dynamic Programming - Successful and Unsuccessful Searches
Master optimal binary search tree construction using dynamic programming, considering both successful and unsuccessful searches. Enhance your understanding of efficient data structures and algorithms.
Data Structure and Algorithms in Java
Comprehensive exploration of data structures and algorithms in Java, covering fundamental concepts, implementation, and analysis of various techniques for efficient programming and problem-solving.
Optimal Control, Guidance and Estimation
Comprehensive exploration of optimal control theory, guidance systems, and estimation techniques for aerospace applications, covering LQR, dynamic programming, and Kalman filters.
Dynamic Programming
Explore dynamic programming techniques for solving complex algorithmic problems efficiently, including coin change, knapsack, and longest common subsequence.
Algorithms in Java
Learn Java algorithm implementation, covering analysis, search methods, sorting techniques, and programming paradigms like greedy and dynamic. Gain practical skills through hands-on examples and real-world applications.
Tower of Hanoi in Python
Learn to solve the Tower of Hanoi puzzle using Python, covering concepts like dynamic programming and recursion. Gain practical coding skills and understand complexity analysis.
Greedy Programming in Java
Learn greedy programming in Java, covering concepts, examples, and implementations of Fractional Knapsack and Prim's Algorithm for minimal spanning trees. Gain practical skills in solving optimization problems efficiently.
Seam Carving: Live Coding Session - MIT 18.S191 Fall 2020 - Week 2
Live coding session demonstrating seam carving algorithm implementation in Julia, covering energy calculation, seam generation, and image manipulation techniques for content-aware image resizing.