Introduction to Programming and Problem-Solving Using Scala
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In this course you will discover the elements of the functional programming style and learn how to apply them usefully in your daily programming tasks. You will also develop a solid foundation for reasoning about functional programs, by touching upon proofs of invariants and the tracing of execution symbolically.
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The course is hands on; most units introduce short programs that serve as illustrations of important concepts and invite you to play with them, modifying and improving them. The course is complemented by a series programming projects as homework assignments.
Recommended background: You should have at least one year programming experience. You should have some familiarity using the command line.
In this course you will learn how to apply the functional programming style in the design of larger applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators.
Several parts of this course deal with the question how functional programming interacts with mutable state.
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We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Learning Outcomes. By the end of this course you will be able to: - recognize and apply design principles of functional programs, - design functional libraries and their APIs, - competently combine functions and state in one program, - understand reasoning techniques for programs that combine functions and state, - write simple functional reactive applications.
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library.
Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas.
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This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections.
Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.
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By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience.
To get started, click the course card that interests you and enroll. Therefore, the current incarnation of helisa uses jenetics as a backend. For this blog, we are going to be using the jenetics library, with the slight caveat that it does not have a stable Scala API. However, a frontend called helisa has been written by Yours Truly that allows for exactly that.
Now that we have all the basic elements of our problem encoding, we can have a go at an evolutionary run! We need some sort of manager for our process — in helisa parlance, this is called an Evolver.
To create it, we need:. To manage iterating the evolutionary process, we currently have a couple of options:. This will print something like:.
As is apparent, given enough iterations, the algorithm eventually arrives at a good answer. However, genetic algorithms are much more useful for e. In general, GAs help in situations where:.
This applies for both topics like improving other machine learning algorithms you can in fact witness some examples in the papers linked above or stuff like your VM configuration. This short article provides a very basic overview of genetic algorithms. In the meantime, you can continue exploring the subject by:. Happy programming, and stay tuned! Looking for Scala and Java Experts? Functional Programming in Scala Specialization on Coursera. The Specialization provides a hands-on introduction to functional programming using Scala.
You can access the courses material and exercises by either signing up for the specialization or auditing the courses individually. The specialization has the following courses. Course videos are available in YouTube for free. Some of the courses below. Try Scala In Your Browser!