Graduate

C9 Lectures: Introduction to Algorithms and Computational Complexity (Yuri Gurevich)

Collection: 
C9 Lectures
Author: 
Yuri Gurevich
Year: 
2010
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
Introduction to Algorithms and Computational Complexity,

C9 Lectures: Introduction to Algorithms and Computational Complexity (Yuri Gurevich)

Collection: 
C9 Lectures
Author: 
Yuri Gurevich
Year: 
2010
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
Introduction to Algorithms and Computational Complexity,

C9 Lectures: Introduction to Algorithms and Computational Complexity (Yuri Gurevich)

Collection: 
C9 Lectures
Author: 
Yuri Gurevich
Year: 
2010
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
Introduction to Algorithms and Computational Complexity,

Machine Learning

Collection: 
openAcademy
Author: 
Andrew Ng
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development.

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.

Advanced Operating Systems Structures and Implementation (John Kubiatowicz)

Collection: 
Individual Authors
Author: 
John Kubiatowicz
Year: 
2014
Conditions of Use: 
FreeVideoLectures
Media Format: 
Material Type: 
Description: 
The purpose of this course is to teach the design of Operating Systems through both academic study and by making modifications to a modern OS (Linux). Topics we will cover include concepts of operating systems and systems programming; utility programs, subsystems, multiple-program systems; processes, interprocess communication, and synchronization; memory allocation, segmentation, paging; loading and linking, libraries; resource allocation, scheduling, performance evaluation; I/O systems, storage devices, file systems; basic networking, protocols, and distributed file systems, protection, security, and privacy.

Machine Learning and Computational Statistics (David Sontag )

Collection: 
Individual Authors
Author: 
David Sontag
Year: 
2014
Conditions of Use: 
Attribution-NonCommercial-ShareAlike 2.0 Generic
Level: 
Media Format: 
Material Type: 
Description: 
Machine learning is an exciting and fast-moving field at the intersection of computer science, statistics, and optimization with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news). Machine learning and computational statistics also play a central role in data science. In this graduate-level class, students will learn about the theoretical foundations of machine learning and computational statistics and how to apply these to solve new problems. This is a required course for the MS in Data Science and should be taken in the first year of study; it is also suitable for MS and Ph.D. students in Computer Science and related fields.

Advanced Robotics (Pieter Abbeel)

Collection: 
Individual Authors
Author: 
Pieter Abbeel
Year: 
2011
Conditions of Use: 
Attribution 3.0 Unported
Level: 
Media Format: 
Material Type: 
Description: 
This is a graduate course on robotics for computers scientists and those with an engineering or physics background. Course topics include: Estimation: Bayes filters, KF, EKF, UKF, particle filter, occupancy grid mapping, EKF slam, GraphSLAM, SEIF, FastSLAM; Optimal Control: Globally Optimal Control through Discretization, Locally Optimal Control through Sequential Quadratic Programming, MPC, LQR, LQG, iterative versions;Motion Planning: RRT , A* RGB-D and Point Clouds: features, Ransac, Hough, instance retrieval/detection; Manipulation and Grasping: grasp quality metrics, grasp strategies, caging; Reinforcement Learning: policy gradient

Open Networks

Collection: 
Free Technology Academy
Author: 
Enric Peig Olivé
Year: 
2014
Conditions of Use: 
Attribution-ShareAlike 3.0 Unported
Media Format: 
Material Type: 
Description: 
This coursebook explores the different aspects of open technologies that are at the foundations of modern computer networks. The Internet is built on top of open protocols and open standards. Open networks do not belong to anybody because the devices they use are made available by the members of the community. Communities are enabled by open network design and innovation emerges. That is the foundation of P2P production and distribution models, IP telephony and multimedia streaming technologies.

Data Security and Privacy: Legal, Policy and Enterprise Issues

Collection: 
Open.Michigan
Author: 
Don Blumenthal
Year: 
2010
Conditions of Use: 
Attribution-ShareAlike 3.0 Unported
Level: 
Media Format: 
Material Type: 
Description: 
This course examines security issues related to the safeguarding of sensitive personal and corporate information against inadvertent disclosure; policy and societal questions concerning the value of security and privacy regulations, the real-world effects of data breaches on individuals and businesses, and the balancing of interests among individuals, government, and enterprises; current and proposed laws and regulations that govern data security and privacy; private-sector regulatory efforts and self-help measures; emerging technologies that may affect security and privacy concerns; and issues related to the development of enterprise data security programs, policies, and procedures that take into account the requirements of all relevant constituencies, e.g., technical, business, and legal.

Networks: Theory and Application

Collection: 
Open.Michigab
Author: 
Lada Adamic
Year: 
2009
Conditions of Use: 
Attribution-ShareAlike 3.0 Unported
Level: 
Media Format: 
Description: 
The course covers topics in network analysis, from social networks to applications in information networks such as the Internet. I will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval.

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