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This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
- Course related:
- COMP3011 Design and Analysis of Algorithms, COMP1001 Problem Solving Methodology in Information Technology, COMP4434 Artificial Intelligence, and COMP2011 Data Structures
- Subjects:
- Human-Computer Interaction and Computing
- Keywords:
- Computer programming Computer science Python (Computer program language) Artificial intelligence
- Resource Type:
- Courseware
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Courseware
In this card, we are going to help you understand the general concept of Binary Search.
Binary Search is one of the most fundamental and useful algorithms in Computer Science. It describes the process of searching for a specific value in an ordered collection.
Terminology used in Binary Search:
(1) Target - the value that you are searching for
(2) Index - the current location that you are searching
(3) Left, Right - the indicies from which we use to maintain our search Space
(4) Mid - the index that we use to apply a condition to determine if we should search left or right
- Course related:
- COMP3011 Design and Analysis of Algorithms
- Subjects:
- Computing
- Keywords:
- Computer algorithms
- Resource Type:
- Courseware
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Courseware
Stanford Engineering Everywhere (SEE) expands the Stanford experience to students and educators online and at no charge. A computer and an Internet connection are all you need. The SEE course portfolio includes one of Stanford's most popular sequences: the three-course Introduction to Computer Science, taken by the majority of Stanford’s undergraduates, as well as more advanced courses in artificial intelligence and electrical engineering.
- Course related:
- EE1D01 Electrical Science for Everyone
- Subjects:
- Electronic and Information Engineering, Biomedical Engineering, Mechanical Engineering, and Computing
- Keywords:
- Engineering Computer science
- Resource Type:
- Courseware
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Courseware
In this course, you will walk away with an up-to-date examination of the maturing FinTech industry and an understanding of the technologies set to shape the future of finance. Insight into who is currently adopting and driving financial technology innovation and the potential for partnerships between incumbents, start-ups and investors. The ability to critically assess the future of the financial services industry, through exploring complex real-world problems and how FinTech can be used to find solutions.A strategic framework to apply within your own role, and the opportunity to share this with like-minded professionals at an additional conference week.
- Course related:
- COMP4142 E-Payment and Cryptocurrency and COMP5521 Distributed Ledger Technology
- Subjects:
- Finance and Computing
- Keywords:
- Financial services industry -- Technological innovations Finance -- Technological innovations
- Resource Type:
- Courseware
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Courseware
ArchiStar Academy has world class software and training for architects, engineers and universities students.
- Subjects:
- Computing
- Keywords:
- Design Technology
- Resource Type:
- Courseware
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Courseware
This is an accelerated introduction to MATLAB® and its popular toolboxes. Lectures are interactive, with students conducting sample MATLAB problems in real time. The course includes problem-based MATLAB assignments. Students must provide their own laptop and software. This is great preparation for classes that use MATLAB.
- Subjects:
- Computing
- Keywords:
- Engineering mathematics -- Data processing MATLAB Numerical analysis -- Computer programs
- Resource Type:
- Courseware
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Courseware
This course covers the basics of J2ME and explores mobile imaging and media creation, GPS location, user-centered design, usability testing, and prototyping. Java experience is recommended.
- Subjects:
- Computing
- Keywords:
- Mobile apps Mobile computing Cell phone systems Application software -- Development
- Resource Type:
- Courseware
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Courseware
This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. It covers the topics including multilevel implementation strategies, definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lower-level elements. It also includes analysis of potential concurrency, precedence constraints and performance measures, pipelined and multidimensional systems, instruction set design issues and architectural support for contemporary software structures.
- Subjects:
- Electrical Engineering and Computing
- Keywords:
- Digital electronics
- Resource Type:
- Courseware
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Courseware
This course introduces the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.
- Subjects:
- Computing
- Keywords:
- Database management
- Resource Type:
- Courseware
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Courseware
The course addresses dynamic systems, i.e., systems that evolve with time. Typically these systems have inputs and outputs; it is of interest to understand how the input affects the output (or, vice-versa, what inputs should be given to generate a desired output). In particular, this course will concentrates on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy certain linearity and time-invariance conditions.
- Subjects:
- Computing
- Keywords:
- Mathematical models Dynamics System theory
- Resource Type:
- Courseware
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Courseware
This is an interdisciplinary, project-based course, centered around a design project in which small teams of students work closely with a person with a disability in the Cambridge area to design a device, piece of equipment, app, or other solution that helps them live more independently.
- Subjects:
- Biomedical Engineering, Mechanical Engineering, Rehabilitation Sciences, Computing, and Electrical Engineering
- Keywords:
- Self-help devices for people with disabilities
- Resource Type:
- Courseware
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Courseware
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
- Subjects:
- Computing
- Keywords:
- Pattern perception -- Statistical methods Machine learning
- Resource Type:
- Courseware
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Courseware
This course examines human-computer interaction in the context of graphical user interfaces. The course covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Deliverables include short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments.
- Subjects:
- Computing
- Keywords:
- Human-computer interaction User interfaces (Computer systems)
- Resource Type:
- Courseware
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Courseware
This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.
- Subjects:
- Computing
- Keywords:
- Algorithms
- Resource Type:
- Courseware
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Courseware
This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.
- Subjects:
- Computing
- Keywords:
- Artificial intelligence
- Resource Type:
- Courseware
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Courseware
6.858 Computer Systems Security is a class about the design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
- Subjects:
- Computing
- Keywords:
- Data protection Computer security Computer networks -- Security measures Data encryption (Computer science)
- Resource Type:
- Courseware
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Courseware
Students of this course will develop a broad understanding of Lean/Six Sigma principles and practices, build capability to implement Lean/Six Sigma initiatives in manufacturing operations, and learn to operate with awareness of Lean/Six Sigma at the enterprise level. All course materials are organized around a common "single-point lesson" (SPL) format, with some of the SPLs provided by the instructor and guests and with some developed and delivered by student teams.
- Subjects:
- Management and Computing
- Keywords:
- Quality control Six sigma (Quality control stard)
- Resource Type:
- Courseware
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Courseware
This course is for all of those struggling with data analysis. You will learn: - Overcome data analysis challenges in your work and research - Increase your productivity and make better business decisions - Enhance your data analysis skills using spreadsheets - Learn about advanced spreadsheet possibilities like array formulas and pivottables - Learn about Excel 2013 features like PowerPivot & PowerMap - Learn to organize and test your spreadsheets
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Courseware
This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. You will learn: - How to make data come to life with well-known types of visualizations such as line and bar graphs and new types of visualizations such as spark lines, contour plots and population pyramids. - How to create dashboards in Excel based on live data that can meet managerial and business needs. - How to connect data from different sources, such as the web and exports from your CRM, ERP, SAP or data warehouse. - Some hands-on data science and how to use actionable analysis tools. - Deep dive into known tools like PivotTables and introduce new ones like the analysis toolpak
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Courseware
The purpose of this course is to learn how to specify the behavior of embedded systems and to experience the design of a provably correct system. In this course you will learn how to formally specify requirements and to prove (or disprove) them on the behaviour. With a practical assignment you will experience how to apply the techniques in practice.
- Subjects:
- Computing
- Keywords:
- Embedded computer systems
- Resource Type:
- Courseware