This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.

Survey of principles underlying the structure and function of the nervous system, integrating molecular, cellular, and systems approaches. Topics: development of the nervous system and its connections, cell biology or neurons, neurotransmitters and synaptic transmission, sensory systems of the brain, the neuroendocrine system, the motor system, higher cortical functions, behavioral and cellular analyses of learning and memory. First half of an intensive two-term survey of brain and behavioral studies for first-year graduate students. Open to graduate students in other departments, with permission of instructor.

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

An introduction to human information processing and learning; topics include the nature of mental representation and processing; the architecture of memory; pattern recognition; attention; imagery and mental codes; concepts and prototypes; reasoning and problem solving.

The purpose of this course is to cultivate an understanding of modern computing technology through an in-depth study of the interface between hardware and software. The student will study the history of modern computing technology before learning about modern computer architecture, then the recent switch from sequential processing to parallel processing. Upon completion of this course, students will be able to: identify important advances that have taken place in the history of modern computing and discuss some of the latest trends in computing industry; explain how programs written in high-level programming language, such as C or Java, can be translated into the language of the hardware; describe the interface between hardware and software and explain how software instructs hardware to accomplish desired functions; demonstrate an understanding of the process of carrying out sequential logic design; demonstrate an understanding of computer arithmetic hardware blocks and floating point representation; explain how a hardware programming language is executed on hardware and how hardware and software design affect performance; demonstrate an understanding of the factors that determine the performance of a program; demonstrate an understanding of the techniques that designers use to improve the performance of programs running on hardware; demonstrate an understanding of the importance of memory hierarchy in computer design and explain how memory design impacts overall hardware performance; demonstrate an understanding of storage and I/O devices, their performance measurement, and redundant array of inexpensive disks (more commonly referred to by the acronym RAID) technology; list the reasons for and the consequences of the recent switch from sequential processing to parallel processing in hardware manufacture and explain the basics of parallel programming. (Computer Science 301)

As an expert on cutting-edge digital displays, Mary Lou Jepsen studies how to show our most creative ideas on screens. And as a brain surgery patient herself, she is driven to know more about the neural activity that underlies invention, creativity, thought. She meshes these two passions in a rather mind-blowing talk on two cutting-edge brain studies that might point to a new frontier in understanding how (and what) we think.

In this course, the student will learn the theoretical and practical aspects of algorithms and Data Structures. The student will also learn to implement Data Structures and algorithms in C/C++, analyze those algorithms, and consider both their worst-case complexity and practical efficiency. Upon successful completion of this course, students will be able to: Identify elementary Data Structures using C/C++ programming languages; Analyze the importance and use of Abstract Data Types (ADTs); Design and implement elementary Data Structures such as arrays, trees, Stacks, Queues, and Hash Tables; Explain best, average, and worst-cases of an algorithm using Big-O notation; Describe the differences between the use of sequential and binary search algorithms. (Computer Science 201)

Idriz is passionate about teaching others how to improve their memories and believes that with the right practice, almost everyone can get a super-memory.

Principles of supervisory control and telerobotics. Different levels of automation are discussed, as well as the allocation of roles and authority between humans and machines. Human-vehicle interface design in highly automated systems. Decision aiding. Tradeoffs between human control and human monitoring. Automated alerting systems and human intervention in automatic operation. Enhanced human interface technologies such as virtual presence. Performance, optimization, and social implications of the human-automation system. Examples from aerospace, ground, and undersea vehicles, robotics, and industrial systems. Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines. These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes. Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems. Topics refer to applications in aerospace, industrial and transportation systems.

Immunology is the study of our immune system, a highly sophisticated system that defends us against all disease-causing invaders by identifying and neutralizing such threats. As with any system in our body, when the immune system malfunctions, disease can result. In this course the student will take a look at what happens when an inappropriate immune response takes place. (Biology 407)

Broad coverage of technology concepts underlying modern computing and information management. Topics include computer architecture and operating systems, relational database systems, graphical user interfaces, networks, client/server systems, enterprise applications, cryptography, and the web. Hands-on exposure to internet services, Microsoft Access database management system, and Lotus Notes. Information Technology I helps students understand technical concepts underlying current and future developments in information technology. There will be a special emphasis on networks and distributed computing. Students will also gain some hands-on exposure to powerful, high-level tools for making computers do amazing things, without the need for conventional programming languages. Since 15.564 is an introductory course, no knowledge of how computers work or are programmed is assumed.

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Alternate years.

"The mammalian brain easily outperforms any computer. It adapts and changes constantly. Most importantly, the brain enables us to continuously learn and remember. What are the molecular mechanisms that lead to learning and memory? What are the cellular roles that activity-regulated gene products play to implement changes in the brain?How do nerve cells, their connections (synapses), and brain circuits change over time to store information? We will discuss the molecular mechanisms of neuronal plasticity at the synaptic, cellular and circuit levels, especiallysynapse formation,synaptic growth and stabilization,synaptic transmission,axonal and dendritic outgrowth, andcircuit formationWe will learn about the roles of some activity-regulated genes as well as the tools and techniques employed in modern neuroscience. Our goal will be to understand molecular mechanisms the brain employs to accomplish learning and memory.This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching."

The Latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC.

This site dissects a sheep brain to show us the anatomy of memory. See works of an artist who paints entirely from memory. (Compare his paintings to photos of places.) Play interactive games that test your memory -- learn ways to improve it. Discover why some things are easier to remember than others (droodles game). Which facial features help us remember a face? Which image of the penny is correct? Try a mnemonic device called elaborative encoding.

Roles of neural plasticity in learning and memory and in development of invertebrates and mammals. An in-depth critical analysis of current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Discussion of original papers supplemented by introductory lectures.

The course presents an overview of the history and structure of modern operating systems, analyzing in detail each of the major components of an operating system, and exploring more advanced topics in the field, such as security concerns. Upon successful completion of this course, the student will be able to: explain what an operating system does and how it is used; identify the various components of a computer system and how they interact with an operating system; describe the differences between a 32-bit and 64-bit operating system; explain the different types of operating systems and the major ones in use today; discuss the importance and use of threads and processes in an operating system; describe concurrency; explain the difference between a thread and a process; discuss context switching and how it is used in an operating system; describe synchronization; explain a race condition; discuss interprocess communication; describe how semaphores can be used in an operating system; discuss three of the classic synchronization problems; explain the alternatives to semaphores; discuss CPU scheduling and its relevance to operating systems; explain the general goals of CPU scheduling; describe the differences between pre-emptive and non-preemptive scheduling; discuss four CPU scheduling algorithms; explain what deadlock is in relation to operating systems; discuss deadlock prevention, avoidance, and their differences; describe deadlock detection and recovery; explain the memory hierarchy; discuss how the operating system interacts with memory; describe how virtual memory works; discuss three algorithms for dynamic memory allocation; explain methods of memory access; describe paging and page replacement algorithms; describe a file system and its purpose; discuss various file allocation methods; explain disk allocation and associated algorithms; discuss types of security threats; describe the various types of malware; explain basic security techniques; explain basic networking principles; discuss protocols and how they are used; explain reference models, particularly TCP/IP and OSI. (Computer Science 401)

Memory is not a unitary faculty, but rather consists of multiple forms of learning that differ in their operating characteristics and neurobiological substrates. This seminar will consider current debates regarding the cognitive and neural architectures of memory, specifically focusing on recent efforts to address these controversies through application of functional neuroimaging (primarily fMRI and PET).

In this math lesson, learners practice addition facts (with sums of 5, 6, 7, and 8) in a concentration-game format using dominoes. Then, learners generate sums of given numbers using a calculator, record the sums on a hundreds chart and look for patterns. This lesson guide includes questions for learners, assessment options, extensions, and reflection questions.

In this course we will discover how innovative technologies combined with profound hypotheses have given rise to our current understanding of neuroscience. We will study both new and classical primary research papers with a focus on the plasticity between synapses in a brain structure called the hippocampus, which is believed to underlie the ability to create and retrieve certain classes of memories. We will discuss the basic electrical properties of neurons and how they fire. We will see how firing properties can change with experience, and we will study the biochemical basis of these changes. We will learn how molecular biology can be used to specifically change the biochemical properties of brain circuits, and we will see how these circuits form a representation of space giving rise to complex behaviors in living animals. A special emphasis will be given to understanding why specific experiments were done and how to design experiments that will answer the questions you have about the brain. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.