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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:
- COMP1001 Problem Solving Methodology in Information Technology, COMP3011 Design and Analysis of Algorithms, COMP2011 Data Structures, and COMP4434 Artificial Intelligence
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Computer programming Computer science Artificial intelligence Python (Computer program language)
- 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, Data Science and Artificial Intelligence
- 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:
- Biomedical Engineering, Electronic and Information Engineering, Mechanical Engineering, and Computing, Data Science and Artificial Intelligence
- 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:
- Computing, Data Science and Artificial Intelligence and Finance
- 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, Data Science and Artificial Intelligence
- 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, Data Science and Artificial Intelligence
- Keywords:
- MATLAB Numerical analysis -- Computer programs Engineering mathematics -- Data processing
- 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, Data Science and Artificial Intelligence
- 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:
- Computing, Data Science and Artificial Intelligence and Electrical Engineering
- 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, Data Science and Artificial Intelligence
- 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, Data Science and Artificial Intelligence
- Keywords:
- Mathematical models Dynamics System theory
- Resource Type:
- Courseware
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