Posts Tagged ‘Stanford’

PPC Ian Stanford Online Marketing Presentation – Part 3

I speak at Stanford Graduate School of Business about online marketing. I cover the following Internet marketing topics: pay per click (PPC), search engine optimization (SEO), social media, and more. In today’s video I’m excited to discuss SEO link building strategies including blog commentating, guest interviews, and SEO-optimized press releases. This is part 3 of 6. www.ppcian.com
Video Rating: 5 / 5

15

12 2012

Lecture 2 | Convex Optimization I (Stanford)

Guest Lecturer Jacob Mattingley covers convex sets and their applications in electrical engineering and beyond for thecourse, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: www.youtube.com EE 364A Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com

16

06 2012

Lecture 11 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how statistical estimation can be used in convex optimization for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: www.youtube.com EE 364A Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com
Video Rating: 5 / 5

02

06 2012

Lecture 13 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues his lecture on Conjugate Gradient Methods and then starts lecturing on the Truncated Newton Method. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: www.youtube.com EE364B Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube www.youtube.com
Video Rating: 5 / 5

24

05 2012

Lecture 12 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on geometric problems in the context of electrical engineering and convex optimization for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: www.youtube.com EE 364A Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com
Video Rating: 5 / 5

24

05 2012

Lecture 15 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues lecturing on L1 Methods for Convex-Cardinality Problems. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. EE 364B Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford on YouTube: www.youtube.com
Video Rating: 5 / 5

22

04 2012

Lecture 8 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd introduces primal and dual decomposition methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: www.youtube.com EE364B Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube www.youtube.com
Video Rating: 5 / 5

10

04 2012

Lecture 18 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd’s final lecture of the quarter is on Branch-and-bound methods. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Complete Playlist for the Course: www.youtube.com EE364B Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube www.youtube.com
Video Rating: 5 / 5

26

03 2012

Lecture 6 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on convex optimization problems for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: www.youtube.com EE 364A Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com

23

03 2012

Lecture 5 | Convex Optimization I (Stanford)

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the different problems that are included within convex optimization for the course, Convex Optimization I (EE 364A). Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. Complete Playlist for the Course: www.youtube.com EE 364A Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com
Video Rating: 4 / 5

28

02 2012


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