Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

This class builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching students to program while solving puzzles. Python syntax and semantics required to understand the code are explained as needed for each puzzle.

How could random numbers speed up a decision algorithm? Created by Brit Cruise.

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Advances in metagenomic sequencing have allowed for the identification of countless novel bacterial taxa in environmental samples. However, due to a lack of appropriate computational tools, the plasmids contained by many of these bacteria have received far less attention. That has restricted research into the important genetic processes plasmids are responsible for, such as horizontal gene transfer and antibiotic resistance. To address this gap, researchers recently developed the Sequence Contents-Aware Plasmid Peeler (SCAPP). An open-source Python package, SCAPP builds upon a previously developed algorithm and uses biological data to assemble plasmid sequences from metagenomic samples. SCAPP was found to outperform existing metagenomic plasmid assembly tools when tested on simulated metagenomes and real human gut microbiome samples. SCAPP could also assemble novel and clinically relevant plasmid sequences in generated samples..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course covers a number of advanced “selected topics” in the field of cryptography. The first part of the course tackles the foundational question of how to define security of cryptographic protocols in a way that is appropriate for modern computer networks, and how to construct protocols that satisfy these security definitions. For this purpose, the framework of “universally composable security” is studied and used. The second part of the course concentrates on the many challenges involved in building secure electronic voting systems, from both theoretical and practical points of view. In the third part, an introduction to cryptographic constructions based on bilinear pairings is given.

In this undergraduate level seminar series, topics vary from year to year. Students present and discuss the subject matter, and are provided with instruction and practice in written and oral communication. Some experience with proofs required. The topic for fall 2008: Computational algebra and algebraic geometry.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Understanding how human decision-making and preferences manifest before conscious thought has long challenged researchers focused on cognitive and information science. Now, the field of neuromarketing – a discipline that looks at the neurocognitive underpinnings of consumer behavior – is starting to uncover, in amazing detail, exactly how the brain goes about recognizing a brand. An international research team based in Auckland University of Technology and Nottingham Trent University has devised a new machine learning method that tracks brain responses to logos on the millisecond timescale…even before conscious thoughts are formed. Their results shed light on the early spikes in brain activity that are tied to brand awareness. The method utilizes one of the most promising recent trends in artificial intelligence research: spiking neural networks. These networks use algorithms loosely modeled on the behavior of the human brain to recognize patterns in sets of streaming data..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware).

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5509 (Theory of Parallel Systems).

Coders use a variety of blocks and sprites to create a short story about a monster under the bed (or in the closet). The purpose of this project is to apply previously learned concepts in a new context and to learn how to modify a backdrop to make it look like nighttime.

This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up:

*Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest.

*Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages.

*Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work.

Finally, many present this material out of context and without motivation: it’s just one damn data structure after another!

I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right.

This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms.

I have made difficult decisions about what to leave out, but I have made some compromises. I include a few topics that most readers will never use, but that they might be expected to know, possibly in a technical interview. For these topics, I present both the conventional wisdom as well as my reasons to be skeptical.

This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter.

This book is intended for college students in computer science and related fields, as well as professional software engineers, people training in software engineering, and people preparing for technical interviews.

I assume that the reader knows Java at an intermediate level, but I explain some Java features along the way, and provide pointers to supplementary material.

People who have read Think Java or Head First Java are prepared for this book.

This interactive lesson helps students understand how companies use algorithms to sort job applicants. It also encourages students to reflect on how digital data mining also can contribute to the hiring process. Students examine resumes and digital data to consider the ways in which our data may open or close opportunities in an increasingly digitized hiring market.

This is a classroom activity report on teaching algorithms as part of a second course in computer programming. Teaching an algorithm in an introductory level programming class is often a dry task for the instructor and the rewards for the student are abstract. To make the learning of algorithms and software more rewarding, this assignment employs a Rubik’s cube.

After completing the associated lesson, students test their understanding in two programming tasks that utilize LEGO MINDSTORMS(TM) NXT robots and sound/touch sensors. In the first challenge, students become acquainted with wait blocks by designing programs to simply make robots move forward until "hearing" a noise, and then turn left. The second, more challenging activity pushes students to fully understand the potential of wait blocks. They create programs that make the robots change speed several times when a touch sensor is pressed. Students gain practice in the iterative design-program-test-redesign process. A PowerPoint® presentation, pre/post quizzes and worksheet are provided.

Students are introduced to the basic concepts of computer programs, algorithms and programming. Using a few blindfolds and a simple taped floor maze exercise, students come to understand that computers rely completely upon instructions given in programs and thus programs must be comprehensive and thorough. Then students learn to program using the LEGO MINDSTORMS(TM) NXT software. They create and test basic programs, first using just the LEGO NXT intelligent brick, and then using basic movement commands with the LEGO NXT software on computers. A detailed PowerPoint® presentation, plus a worksheet and pre/post quizzes are provided.

This video lesson will introduce students to algorithmic thinking through the use of a popular field in graph theory—social networking. Specifically, by acting as nodes in a graph (i.e. people in a social network), the students will experientially gain an understanding of graph theory terminology and distance in a graph (i.e. number of introductions required to meet a target person). Once the idea of distance in a graph has been built, the students will discover Dijkstra's Algorithm. The lesson should take approximately 90 minutes and can be comfortably partitioned across two class sessions if necessary (see the note in the accompanying Teacher Guide). There are no special supplies needed for this class and all necessary hand-outs can be downloaded from this website.

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