Looking for programming study group / buddies
I live in Central District and recently started studying programming online. Specifically I'm taking the Princeton Algorithms I course on Coursera. It's an interesting course and I 'm enjoying it, but I feel kind of lonely not having any fellow classmates to complain, drink, and collaborate with. I've been turning to my girlfriend for this purpose so far and I think she's losing her mind. Most people don't wanna talk about why your code won't compile. Was wondering if anyone else in a similar boat would want to get together like once a week to just hang out for camaraderie? I'd be willing to go to other parts of the city but not outside of Seattle. I don't mind if you're studying the same course or some other computerprogrammingrelated course. Please be at least kind of a programming noob, as I am looking for people in a similar boat. In my case I have some programming experience from work but still have a lot to learn and have never had a 'programmer' job.
UTCâ06.00 Novice English
Description
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph and stringprocessing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Syllabus
 Course Introduction
 Welcome to Algorithms, Part I.
 UnionâFind
 We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the unionâfind data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the unionâfind data type to the percolation problem from physical chemistry.
 Analysis of Algorithms
 The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs.
 Stacks and Queues
 We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singlylinked list or a resizing array. We introduce two advanced Java featuresâgenerics and iteratorsâthat simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.
 Elementary Sorts
 We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm.
 Mergesort
 We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottomup version. We prove that any comparebased sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability.
 Quicksort
 We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys.
 Priority Queues
 We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision.
 Elementary Symbol Tables
 We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance.
 Balanced Search Trees
 In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2â3 trees, which are easy to analyze but hard to implement. Next, we consider redâblack binary search trees, which we view as a novel way to implement 2â3 trees as binary search trees. Finally, we introduce Btrees, a generalization of 2â3 trees that are widely used to implement file systems.
 Geometric Applications of BSTs
 We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kdtrees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.
 Hash Tables
 We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tablesâseparate chaining and linear probing. Both strategies yield constanttime performance for search and insert under the uniform hashing assumption.
 Symbol Table Applications
 We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
Get notified about new study groups every week!
Looking for programming study group / buddies
I live in Central District and recently started studying programming online. Specifically I'm taking the Princeton Algorithms I course on Coursera. It's an interesting course and I 'm enjoying it, but I feel kind of lonely not having any fellow classmates to complain, drink, and collaborate with. I've been turning to my girlfriend for this purpose so far and I think she's losing her mind. Most people don't wanna talk about why your code won't compile. Was wondering if anyone else in a similar boat would want to get together like once a week to just hang out for camaraderie? I'd be willing to go to other parts of the city but not outside of Seattle. I don't mind if you're studying the same course or some other computerprogrammingrelated course. Please be at least kind of a programming noob, as I am looking for people in a similar boat. In my case I have some programming experience from work but still have a lot to learn and have never had a 'programmer' job.
UTCâ06.00 Novice English

TypeOnline Courses

ProviderCoursera

PricingFree to Audit

Duration54 hours

DifficultyIntermediate
 Course Introduction
 Welcome to Algorithms, Part I.
 UnionâFind
 We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the unionâfind data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the unionâfind data type to the percolation problem from physical chemistry.
 Analysis of Algorithms
 The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs.
 Stacks and Queues
 We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singlylinked list or a resizing array. We introduce two advanced Java featuresâgenerics and iteratorsâthat simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.
 Elementary Sorts
 We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm.
 Mergesort
 We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottomup version. We prove that any comparebased sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability.
 Quicksort
 We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys.
 Priority Queues
 We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision.
 Elementary Symbol Tables
 We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance.
 Balanced Search Trees
 In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2â3 trees, which are easy to analyze but hard to implement. Next, we consider redâblack binary search trees, which we view as a novel way to implement 2â3 trees as binary search trees. Finally, we introduce Btrees, a generalization of 2â3 trees that are widely used to implement file systems.
 Geometric Applications of BSTs
 We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kdtrees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.
 Hash Tables
 We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tablesâseparate chaining and linear probing. Both strategies yield constanttime performance for search and insert under the uniform hashing assumption.
 Symbol Table Applications
 We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
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Get notified about new study groups every week!