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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
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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