Online courses directory (779)
In this free online course, Applied Operations Management - Aggregate Planning, you will learn how aggregate planning is used in many businesses to match supply and demand of output over the medium time range of up to approximately 12 months in the future. Aggregate planning allows management to quantify materials and other resources that are to be procured so that the total cost of operations are kept to the minimum over a set period of time. The course begins by introducing the concept of aggregate planning and its use in medium term planning in businesses. You will be introduced to aggregate planning methodologies such as the tabular method and linear programming. You will learn how a tabular approach uses spreadsheets and the values of different variables, such as production by regular workforce and inventory levels can be calculated by using the costs associated with production, overtime, subcontracting, hiring, inventory and backorders. The tabular method is widely used because it is easy to understand and utilize. However, the generated solution may not be optimal and many trials and errors may be needed to find the optimal solution. The course also introduces the linear programming technique and a special type of linear programming known as the Transportation Model, which can be used to obtain aggregate plans that would allow balanced capacity and demand and the minimization of costs. This free online course will be of great interest to all professionals working in the areas operations management or general management who would like to learn more about aggregate planning and the methods used in this important area of operations management. The course will also be of interest to all learners who are interested in operations management as a future career.<br />
This is CS50 AP, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for students in high school, which satisfies the College Board's new AP CS Principles curriculum framework.
Students in high school may receive AP credit for this course provided their school approves the credit and administers the College Board's end-of-year exam. Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are also eligible to receive a verified certificate from HarvardX.
Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!
In recent years, flying robots such as miniature helicopters or quadrotors have received a large gain in popularity. Potential applications range from aerial filming over remote visual inspection of industrial sites to automatic 3D reconstruction of buildings. Navigating a quadrotor manually requires a skilled pilot and constant concentration. Therefore, there is a strong scientific interest to develop solutions that enable quadrotors to fly autonomously and without constant human supervision. This is a challenging research problem because the payload of a quadrotor is uttermost constrained and so both the quality of the onboard sensors and the available computing power is strongly limited.
In this course, we will introduce the basic concepts for autonomous navigation for quadrotors. The following topics will be covered:
- 3D geometry,
- probabilistic state estimation,
- visual odometry, SLAM, 3D mapping,
- linear control.
In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.
The course consists of a series of weekly lecture videos that we be interleaved by interactive quizzes and hands-on programming tasks. For the flight experiments, we provide a browser-based quadrotor simulator which requires the students to write small code snippets in Python.
This course is intended for undergraduate and graduate students in computer science, electrical engineering or mechanical engineering. This course has been offered by TUM for the first time in summer term 2014 on EdX with more than 20.000 registered students of which 1400 passed examination. The MOOC is based on the previous TUM lecture “Visual Navigation for Flying Robots” which received the TUM TeachInf best lecture award in 2012 and 2013.
Do I need to buy a textbook?
No, all required materials will be provided within the courseware. However, if you are interested, we recommend the following additional materials:
- This course is based on the TUM lecture Visual Navigation for Flying Robots. The course website contains lecture videos (from last year), additional exercises and the full syllabus: http://vision.in.tum.de/teaching/ss2013/visnav2013
- Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT Press, 2005.
- Computer Vision: Algorithms and Applications. Richard Szeliski. Springer, 2010.
Do I need to build/own a quadrotor?
No, we provide a web-based quadrotor simulator that will allow you to test your solutions in simulation. However, we took special care that the code you will be writing will be compatible with a real Parrot Ardrone quadrotor. So if you happen to have a Parrot Ardrone quadrotor, we encourage you to try out your solutions for real.
Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.
This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.
Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.
In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Hadoop, R and MOA (Massive Online Analysis).
Topics covered in this course include:
- cloud-based big data analysis;
- predictive analytics, including probabilistic and statistical models;
- application of large-scale data analysis;
- analysis of problem space and data needs;
- understanding of ethical and social concerns of data mining.
By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.
Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.
In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organisational change and the key challenges organizations face when trying to analyse massive data sets.
You will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. You will learn how big data has improved web search and how online advertising systems work.
By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.
The advent of computers transformed science. Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software. Nowadays, scientists can use computers to produce several hypotheses as to how a particular phenomenon works, create computer models using the parameters of each hypothesis, input data, and see which hypothetical model produces an output that most closely mirrors reality. Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology. With computational biology, researchers apply mathematics to biological phenomena, use computer programming and algorithms to artificially create or model the phenomena, and draw from statistics in order to interpret the findings. In this course, you will learn the basic principles and procedures of computational biology. You will also learn various ways in which you can apply computational biology to molecular and cell…