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# Algorithms

Algorithms

ALL CONTENT IN “ALGORITHMS”

### Intro to algorithms

What are algorithms and why should you care? We'll start with an overview of algorithms and then discuss two games that you could use an algorithm to solve more efficiently - the number guessing game and a route-finding game.

### Binary search

Learn about binary search, a way to efficiently search an array of items by halving the search space each time.

### Asymptotic notation

Learn how to use asymptotic analysis to describe the efficiency of an algorithm, and how to use asymptotic notation (Big O, Big-Theta, and Big-Omega) to more precisely describe the efficiency.

### Selection sort

Learn selection sort, a simple algorithm for sorting an array of values, and see why it isn't the most efficient algorithm.

### Insertion sort

Learn insertion sort, another simple but not very efficient way to sort an array of values.

### Recursive algorithms

Learn the concept of recursion, a technique that is often used in algorithms. See how to use recursion to calculate factorial and powers of a number, plus to generate art.

### Towers of Hanoi

Use the recursive technique to solve the Towers of Hanoi, a classic mathematical puzzle and one reportedly faced by monks in a temple.

### Merge sort

Learn merge sort, a more efficient sorting algorithm that relies heavily on the power of recursion to repeatedly sort and merge sub-arrays.

### Quick sort

Learn quick sort, another efficient sorting algorithm that uses recursion to more quickly sort an array of values.

### Graph representation

Learn how to describe graphs, with their edges, vertices, and weights, and see different ways to store graph data, with edge lists, adjacency matrices, and adjacency lists.

Learn how to traverse a graph using breadth-first-search to find a particular node or to make sure you've visited all the notes, traversing one layer at a time.

### Further learning

Ideas of how you could continue your learning journey in algorithms.

SyllabusVideosClass NotesReference Text Books

Analysis, Asymptotic notation, Notions of space and time complexity, Worst and average case analysis;
Design: Greedy approach, Dynamic programming, Divide-and-conquer; Tree and graph traversals, Connected components, Spanning trees, Shortest paths; Hashing, Sorting, Searching.

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to "dial" by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to "dial" by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to "dial" by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

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