2015-04-25
Slavoj Zizek. Lacan’s four discourses and the real. 2014
source : European Graduate School Video Lectures 2015年4月25日
http://www.egs.edu/ Slavoj Zizek, Slovenian philosopher and cultural critic, talking about the theoretical richness of Lacan’s four discourses: the master’s discourse, the hysteric’s discourse, the university discourse, and the analyst’s discourse. What follows is a prolonged discussion of the various guises of the object small a in its imaginary, symbolic and real forms and its relevance for contemporary ideology critique. Public open lecture for the students and faculty of the European Graduate School EGS Media and Communication Studies department program Saas-Fee Switzerland Europe 2014 Slavoj Zizek.
Discrete Stochastic Processes by Robert Gallager (Spring 2011)
# automatic playing for the 25 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT 6.262 Discrete Stochastic Processes, Spring 2011
View the complete course: http://ocw.mit.edu/6-262S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction and Probability Review 1:16:27
2. More Review; The Bernoulli Process 1:08:20
3. Law of Large Numbers, Convergence 1:21:28
4. Poisson (the Perfect Arrival Process) 1:17:14
5. Poisson Combining and Splitting 1:24:32
6. From Poisson to Markov 1:19:17
7. Finite-state Markov Chains; The Matrix Approach 55:34
8. Markov Eigenvalues and Eigenvectors 1:23:38
9. Markov Rewards and Dynamic Programming 1:23:36
10. Renewals and the Strong Law of Large Numbers 1:21:53
11. Renewals: Strong Law and Rewards 1:18:17
12. Renewal Rewards, Stopping Trials, and Wald's Inequality 1:26:21
13. Little, M/G/1, Ensemble Averages 1:14:53
14. Review 1:19:19
15. The Last Renewal 1:15:44
16. Renewals and Countable-state Markov 1:19:40
17. Countable-state Markov Chains 1:23:46
18. Countable-state Markov Chains and Processes 1:16:29
19. Countable-state Markov Processes 1:22:14
20. Markov Processes and Random Walks 1:23:09
21. Hypothesis Testing and Random Walks 1:25:23
22. Random Walks and Thresholds 1:21:17
23. Martingales (Plain, Sub, and Super) 1:22:40
24. Martingales: Stopping and Converging 1:20:44
25. Putting It All Together 1:21:27
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT 6.262 Discrete Stochastic Processes, Spring 2011
View the complete course: http://ocw.mit.edu/6-262S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction and Probability Review 1:16:27
2. More Review; The Bernoulli Process 1:08:20
3. Law of Large Numbers, Convergence 1:21:28
4. Poisson (the Perfect Arrival Process) 1:17:14
5. Poisson Combining and Splitting 1:24:32
6. From Poisson to Markov 1:19:17
7. Finite-state Markov Chains; The Matrix Approach 55:34
8. Markov Eigenvalues and Eigenvectors 1:23:38
9. Markov Rewards and Dynamic Programming 1:23:36
10. Renewals and the Strong Law of Large Numbers 1:21:53
11. Renewals: Strong Law and Rewards 1:18:17
12. Renewal Rewards, Stopping Trials, and Wald's Inequality 1:26:21
13. Little, M/G/1, Ensemble Averages 1:14:53
14. Review 1:19:19
15. The Last Renewal 1:15:44
16. Renewals and Countable-state Markov 1:19:40
17. Countable-state Markov Chains 1:23:46
18. Countable-state Markov Chains and Processes 1:16:29
19. Countable-state Markov Processes 1:22:14
20. Markov Processes and Random Walks 1:23:09
21. Hypothesis Testing and Random Walks 1:25:23
22. Random Walks and Thresholds 1:21:17
23. Martingales (Plain, Sub, and Super) 1:22:40
24. Martingales: Stopping and Converging 1:20:44
25. Putting It All Together 1:21:27
Digital Signal Processing by Alan V. Oppenheim (1975, MIT)
# automatic playing for the 22 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT RES.6-008 Digital Signal Processing, 1975
Set of 20 video lectures for Signals and Systems, an introductory course in analog and digital signal processing, including seismic data processing, communications, speech processing, image processing, consumer electronics, and defense electronics.
View the complete course: http://ocw.mit.edu/RES6-008S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction 17:42
Demonstration 1: Sampling, aliasing, and frequency response, part 1 28:15
Demonstration 2: Sampling, aliasing, and frequency response, part 2 12:05
Lec 2 Discrete time signals and systems, part 1 36:56
Lec 3 Discrete time signals and systems, part 2 43:48
Lec 4 The discrete time Fourier transform 44:08
Lec 5 The z transform 51:01
Lec 6 The inverse z transform 46:57
Lec 7 z-Transform properties 56:28
Lec 8 The discrete Fourier series 43:03
Lec 9 The discrete Fourier transform 47:33
Lec 10 Circular convolution 43:19
Lec 11 Representation of linear digital networks 46:12
Lec 12 Network structures for infinite impulse response (IIR) systems 40:09
Lec 13 Network structures for finite impulse response (FIR) systems and parameter quantization effects in digital filter structures 49:34
Lec 14 Design of IIR digital filters, part 1 47:31
Lec 15 Design of IIR digital filters, part 2 41:10
Lec 16 Digital Butterworth filters 48:39
Lec 17 Design of FIR digital filters 38:27
Lec 18 Computation of the discrete Fourier transform, part 1 48:57
Lec 19 Computation of the discrete Fourier transform, part 2 44:01
Lec 20 Computation of the discrete Fourier transform, part 3 44:58
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT RES.6-008 Digital Signal Processing, 1975
Set of 20 video lectures for Signals and Systems, an introductory course in analog and digital signal processing, including seismic data processing, communications, speech processing, image processing, consumer electronics, and defense electronics.
View the complete course: http://ocw.mit.edu/RES6-008S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction 17:42
Demonstration 1: Sampling, aliasing, and frequency response, part 1 28:15
Demonstration 2: Sampling, aliasing, and frequency response, part 2 12:05
Lec 2 Discrete time signals and systems, part 1 36:56
Lec 3 Discrete time signals and systems, part 2 43:48
Lec 4 The discrete time Fourier transform 44:08
Lec 5 The z transform 51:01
Lec 6 The inverse z transform 46:57
Lec 7 z-Transform properties 56:28
Lec 8 The discrete Fourier series 43:03
Lec 9 The discrete Fourier transform 47:33
Lec 10 Circular convolution 43:19
Lec 11 Representation of linear digital networks 46:12
Lec 12 Network structures for infinite impulse response (IIR) systems 40:09
Lec 13 Network structures for finite impulse response (FIR) systems and parameter quantization effects in digital filter structures 49:34
Lec 14 Design of IIR digital filters, part 1 47:31
Lec 15 Design of IIR digital filters, part 2 41:10
Lec 16 Digital Butterworth filters 48:39
Lec 17 Design of FIR digital filters 38:27
Lec 18 Computation of the discrete Fourier transform, part 1 48:57
Lec 19 Computation of the discrete Fourier transform, part 2 44:01
Lec 20 Computation of the discrete Fourier transform, part 3 44:58
Material (Fall 2005) by Bernhardt Wuensch at MIT
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年6月29日
Symmetry, Structure, and Tensor Properties of Materials
This course covers the derivation of symmetry theory; lattices, point groups, space groups, and their properties; use of symmetry in tensor representation of crystal properties, including anisotropy and representation surfaces; and applications to piezoelectricity and elasticity.
View the complete course at: http://ocw.mit.edu/3-60F05
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1a: Symmetry, Structure, Tensor Properties of Materials 45:33
Lec 1b 45:51
Lec 2a: 50:36
Lec 2b: 52:22
Lec 3a: 54:36
Lec 3b: 49:21
Lec 4a: 57:20
Lec 4b: 48:09
Lec 5a: 54:48
Lec 5b: 36:52
Lec 6a: 50:23
Lec 6b: 38:54
Lec 7a: 59:51
Lec 7b: 35:00
Lec 8a: 49:58
Lec 8b: 42:37
Lec 10a: 35:14
Lec 10b: 48:37
Lec 11a: 46:59
Lec 11b: 50:39
Lec 12a: 53:39
Lec 13a: 58:38
Lec 13b: 39:07
Lec 14a: 18:17
Lec 14b: 1:08:37
Lec 15a: 45:53
Lec 15b: 44:06
Lec 16a: 45:01
Lec 16b: 37:59
Lec 18a: 47:39
Lec 18b: 38:54
Lec 20a: 1:03:30
Lec 20b: 31:22
Lec 21a: 47:31
Lec 21b: 41:17
Lec 22a: 52:21
Lec 23a: 57:56
Lec 23b: 39:12
Lec 24b: 39:32
Lec 24a: 51:28
Lec 26: 1:16:59
source: MIT OpenCourseWare Last updated on 2014年6月29日
Symmetry, Structure, and Tensor Properties of Materials
This course covers the derivation of symmetry theory; lattices, point groups, space groups, and their properties; use of symmetry in tensor representation of crystal properties, including anisotropy and representation surfaces; and applications to piezoelectricity and elasticity.
View the complete course at: http://ocw.mit.edu/3-60F05
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1a: Symmetry, Structure, Tensor Properties of Materials 45:33
Lec 1b 45:51
Lec 2a: 50:36
Lec 2b: 52:22
Lec 3a: 54:36
Lec 3b: 49:21
Lec 4a: 57:20
Lec 4b: 48:09
Lec 5a: 54:48
Lec 5b: 36:52
Lec 6a: 50:23
Lec 6b: 38:54
Lec 7a: 59:51
Lec 7b: 35:00
Lec 8a: 49:58
Lec 8b: 42:37
Lec 10a: 35:14
Lec 10b: 48:37
Lec 11a: 46:59
Lec 11b: 50:39
Lec 12a: 53:39
Lec 13a: 58:38
Lec 13b: 39:07
Lec 14a: 18:17
Lec 14b: 1:08:37
Lec 15a: 45:53
Lec 15b: 44:06
Lec 16a: 45:01
Lec 16b: 37:59
Lec 18a: 47:39
Lec 18b: 38:54
Lec 20a: 1:03:30
Lec 20b: 31:22
Lec 21a: 47:31
Lec 21b: 41:17
Lec 22a: 52:21
Lec 23a: 57:56
Lec 23b: 39:12
Lec 24b: 39:32
Lec 24a: 51:28
Lec 26: 1:16:59
Single Variable Calculus by Herbert Gross
# automatic playing for the 38 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT Calculus Revisited: Single Variable Calculus
Resource Description: Calculus Revisited is a series of videos and related resources that covers the materials normally found in a freshman-level introductory calculus course. The series was first released in 1970 as a way for people to review the essentials of calculus. It is equally valuable for students who are learning calculus for the first time.
About the Instructor: Herbert Gross has taught math as senior lecturer at MIT and was the founding math department chair at Bunker Hill Community College. He is the developer of the Mathematics As A Second Language website, providing arithmetic and algebra materials to elementary and middle school teachers. You can read more about Prof. Gross on his website.
Acknowledgements: Funding for this resource was provided by the Gabriella and Paul Rosenbaum Foundation.
View the complete course at: http://ocw.mit.edu/RES-18-006F10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Preface | MIT Calculus Revisited: Single Variable Calculus 32:06
Unit I Sets, Functions, and Limits: Lec 1 Analytic Geometry 37:42
Unit I: Lec 4 Derivatives and Limits 45:00
Unit I: Lec 5 A More Rigorous Approach to Limits 46:13
Unit I: Lec 6 Mathematical Induction 29:24
Unit II Differentiation: Lec 1 Derivatives of Some Simple Functions 28:17
Unit II: Lec 2 Approximations and Infinitesimals 34:36
Unit II: Lec 3 Composite Functions and the Chain Rule 39:16
Unit II: Lec 4 Differentiation of Inverse Functions 28:55
Unit II: Lec 5 Implicit Differentiation 39:58
Unit II: Lec 6 Continuity 22:49
Unit II: Lec 7 Curve Plotting 31:49
Unit II: Lec 8 Maxima and Minima 34:53
Unit II: Lec 9 Rolle's Theorem and its Consequences 30:28
Unit II: Lec 10 Inverse Differentiation 42:59
Unit II: Lec 11 The Definite Indefinite Integral 29:16
Unit III The Circular Function: Lec 1 Circular Functions 36:01
Unit III: Lec 2 Inverse Circular Functions 26:09
Unit IV The Definite Integral: Lec 1 The Definite Integral 36:37
Unit IV: Lec 2 Marriage of Differential and Integral Calculus 30:31
Unit IV: Lec 3 Three-Dimensional Area 42:06
Unit IV: Lec 4 One-Dimensional Area 36:45
Unit V Transcendental Functions: Lec 1 Logarithms without Exponents 34:46
Unit V: Lec 2 Inverse Logarithms 21:37
Unit V: Lec 3 What a Difference a Sign Makes 27:43
Unit V: Lec 4 Inverse Hyperbolic Functions 29:55
Unit VI More Integration Techniques: Lec 1 Some Basic Recipes 30:29
Unit VI: Lec 2 Partial Functions 32:29
Unit VI: Lec 3 Integration by Parts 27:01
Unit VI: Lec 4 Improper Integrals 29:39
Unit VII Infinite Series: Lec 1 Many Versus Infinite 26:31
Unit VII: Lec 2 Positive Series 34:50
Unit VII: Lec 3 Absolute Convergence 21:09
Unit VII: Lec 4 Polynomial Approximations 32:42
Unit VII: Lec 5 Uniform Convergence 28:57
Unit VII: Lec 6 Uniform Convergence of Power Series 27:04
source: MIT OpenCourseWare Last updated on 2014年7月1日
MIT Calculus Revisited: Single Variable Calculus
Resource Description: Calculus Revisited is a series of videos and related resources that covers the materials normally found in a freshman-level introductory calculus course. The series was first released in 1970 as a way for people to review the essentials of calculus. It is equally valuable for students who are learning calculus for the first time.
About the Instructor: Herbert Gross has taught math as senior lecturer at MIT and was the founding math department chair at Bunker Hill Community College. He is the developer of the Mathematics As A Second Language website, providing arithmetic and algebra materials to elementary and middle school teachers. You can read more about Prof. Gross on his website.
Acknowledgements: Funding for this resource was provided by the Gabriella and Paul Rosenbaum Foundation.
View the complete course at: http://ocw.mit.edu/RES-18-006F10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Preface | MIT Calculus Revisited: Single Variable Calculus 32:06
Unit I Sets, Functions, and Limits: Lec 1 Analytic Geometry 37:42
Unit I: Lec 2 Functions 39:43
Unit I: Lec 3 Inverse Functions 40:39Unit I: Lec 4 Derivatives and Limits 45:00
Unit I: Lec 5 A More Rigorous Approach to Limits 46:13
Unit I: Lec 6 Mathematical Induction 29:24
Unit II Differentiation: Lec 1 Derivatives of Some Simple Functions 28:17
Unit II: Lec 2 Approximations and Infinitesimals 34:36
Unit II: Lec 3 Composite Functions and the Chain Rule 39:16
Unit II: Lec 4 Differentiation of Inverse Functions 28:55
Unit II: Lec 5 Implicit Differentiation 39:58
Unit II: Lec 6 Continuity 22:49
Unit II: Lec 7 Curve Plotting 31:49
Unit II: Lec 8 Maxima and Minima 34:53
Unit II: Lec 9 Rolle's Theorem and its Consequences 30:28
Unit II: Lec 10 Inverse Differentiation 42:59
Unit II: Lec 11 The Definite Indefinite Integral 29:16
Unit III The Circular Function: Lec 1 Circular Functions 36:01
Unit III: Lec 2 Inverse Circular Functions 26:09
Unit IV The Definite Integral: Lec 1 The Definite Integral 36:37
Unit IV: Lec 2 Marriage of Differential and Integral Calculus 30:31
Unit IV: Lec 3 Three-Dimensional Area 42:06
Unit IV: Lec 4 One-Dimensional Area 36:45
Unit V Transcendental Functions: Lec 1 Logarithms without Exponents 34:46
Unit V: Lec 2 Inverse Logarithms 21:37
Unit V: Lec 3 What a Difference a Sign Makes 27:43
Unit V: Lec 4 Inverse Hyperbolic Functions 29:55
Unit VI More Integration Techniques: Lec 1 Some Basic Recipes 30:29
Unit VI: Lec 2 Partial Functions 32:29
Unit VI: Lec 3 Integration by Parts 27:01
Unit VI: Lec 4 Improper Integrals 29:39
Unit VII Infinite Series: Lec 1 Many Versus Infinite 26:31
Unit VII: Lec 2 Positive Series 34:50
Unit VII: Lec 3 Absolute Convergence 21:09
Unit VII: Lec 4 Polynomial Approximations 32:42
Unit VII: Lec 5 Uniform Convergence 28:57
Unit VII: Lec 6 Uniform Convergence of Power Series 27:04
Multivariable Calculus by Herbert Gross
# automatic playing for the 26 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Calculus Revisited: Multivariable Calculus
This course is a study of the calculus of functions of several variables (vector arithmetic and vector calculus).
View the complete course at: http://ocw.mit.edu/RES.18-007F11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Part I: Vector Arithmetic, Lec 1 20:08
Part I: Vector Arithmetic, Lec 2 28:02
Part I: Vector Arithmetic, Lec 3 26:43
Part I: Vector Arithmetic, Lec 4 30:06
Part I: Vector Arithmetic, Lec 5 32:14
Part I: Vector Arithmetic, Lec 6 28:05
Part II: Vector Calculus, Lec 1 38:50
Part II: Vector Calculus, Lec 2 28:24
Part II: Vector Calculus, Lec 3 31:00
Part II: Vector Calculus, Lec 4 28:05
Part III: Partial Derivatives, Lec 1 33:05
Part III: Partial Derivatives, Lec 2 35:33
Part III: Partial Derivatives, Lec 3 33:15
Part III: Partial Derivatives, Lec 4 38:34
Part III: Partial Derivatives, Lec 5 27:20
Part III: Partial Derivatives, Lec 6 29:24
Part IV: Matrix Algebra, Lec 1 47:55
Part IV: Matrix Algebra, Lec 2 41:13
Part IV: Matrix Algebra, Lec 3 45:43
Part IV: Matrix Algebra, Lec 4 28:39
Part IV: Matrix Algebra, Lec 5 34:49
Part V: Multiple Integration, Lec 1 29:59
Part V: Multiple Integration, Lec 2 26:34
Part V: Multiple Integration, Lec 3 33:07
Part V: Multiple Integration, Lec 4 24:08
Part V: Multiple Integration, Lec 5 28:41
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Calculus Revisited: Multivariable Calculus
This course is a study of the calculus of functions of several variables (vector arithmetic and vector calculus).
View the complete course at: http://ocw.mit.edu/RES.18-007F11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Part I: Vector Arithmetic, Lec 1 20:08
Part I: Vector Arithmetic, Lec 2 28:02
Part I: Vector Arithmetic, Lec 3 26:43
Part I: Vector Arithmetic, Lec 4 30:06
Part I: Vector Arithmetic, Lec 5 32:14
Part I: Vector Arithmetic, Lec 6 28:05
Part II: Vector Calculus, Lec 1 38:50
Part II: Vector Calculus, Lec 2 28:24
Part II: Vector Calculus, Lec 3 31:00
Part II: Vector Calculus, Lec 4 28:05
Part III: Partial Derivatives, Lec 1 33:05
Part III: Partial Derivatives, Lec 2 35:33
Part III: Partial Derivatives, Lec 3 33:15
Part III: Partial Derivatives, Lec 4 38:34
Part III: Partial Derivatives, Lec 5 27:20
Part III: Partial Derivatives, Lec 6 29:24
Part IV: Matrix Algebra, Lec 1 47:55
Part IV: Matrix Algebra, Lec 2 41:13
Part IV: Matrix Algebra, Lec 3 45:43
Part IV: Matrix Algebra, Lec 4 28:39
Part IV: Matrix Algebra, Lec 5 34:49
Part V: Multiple Integration, Lec 1 29:59
Part V: Multiple Integration, Lec 2 26:34
Part V: Multiple Integration, Lec 3 33:07
Part V: Multiple Integration, Lec 4 24:08
Part V: Multiple Integration, Lec 5 28:41
Calculus of Complex Variables by Herbert Gross
# automatic playing for the 20 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Calculus Revisited: Calculus of Complex Variables
This course gives an introduction to Complex Variables, Ordinary Differential Equations and Linear Algebra.
View the complete course at: http://ocw.mit.edu/RES18-008
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Part I: Complex Variables, Lec 1: The Complex Numbers 43:37
Part I: Complex Variables, Lec 2: Functions of a Complex Variable 35:08
Part I: Complex Variables, Lec 3: Conformal Mappings 36:00
Part I: Complex Variables, Lec 4: Sequences and Series 33:36
Part I: Complex Variables, Lec 5: Integrating Complex Functions 34:52
Part II: Differential Equations, Lec 1: The Concept of a General Solution 34:33
Part II: Differential Equations, Lec 2: Linear Differential Equations 35:15
Part II: Differential Equations, Lec 3: Solving the Linear Equations L(y) = 0; Constant Coefficients 19:48
Part II: Differential Equations, Lec 4: Undetermined Coefficients 29:27
Part II: Differential Equations, Lec 5: Variations of Parameters 24:54
Part II: Differential Equations, Lec 6: Power Series Solutions 33:29
Part II: Differential Equations, Lec 7: Laplace Transforms 38:35
Part III: Linear Algebra, Lec 1: Vector Spaces 31:43
Part III: Linear Algebra, Lec 2: Spanning Vectors 27:49
Part III: Linear Algebra, Lec 3: Constructing Bases 40:06
Part III: Linear Algebra, Lec 4: Linear Transformations 36:26
Part III: Linear Algebra, Lec 5: Determinants 39:31
Part III: Linear Algebra, Lec 6: Eigenvectors 38:04
Part III: Linear Algebra, Lec 7: Dot Products 41:26
Part III: Linear Algebra, Lec 8: Orthogonal Functions 34:30
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Calculus Revisited: Calculus of Complex Variables
This course gives an introduction to Complex Variables, Ordinary Differential Equations and Linear Algebra.
View the complete course at: http://ocw.mit.edu/RES18-008
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Part I: Complex Variables, Lec 1: The Complex Numbers 43:37
Part I: Complex Variables, Lec 2: Functions of a Complex Variable 35:08
Part I: Complex Variables, Lec 3: Conformal Mappings 36:00
Part I: Complex Variables, Lec 4: Sequences and Series 33:36
Part I: Complex Variables, Lec 5: Integrating Complex Functions 34:52
Part II: Differential Equations, Lec 1: The Concept of a General Solution 34:33
Part II: Differential Equations, Lec 2: Linear Differential Equations 35:15
Part II: Differential Equations, Lec 3: Solving the Linear Equations L(y) = 0; Constant Coefficients 19:48
Part II: Differential Equations, Lec 4: Undetermined Coefficients 29:27
Part II: Differential Equations, Lec 5: Variations of Parameters 24:54
Part II: Differential Equations, Lec 6: Power Series Solutions 33:29
Part II: Differential Equations, Lec 7: Laplace Transforms 38:35
Part III: Linear Algebra, Lec 1: Vector Spaces 31:43
Part III: Linear Algebra, Lec 2: Spanning Vectors 27:49
Part III: Linear Algebra, Lec 3: Constructing Bases 40:06
Part III: Linear Algebra, Lec 4: Linear Transformations 36:26
Part III: Linear Algebra, Lec 5: Determinants 39:31
Part III: Linear Algebra, Lec 6: Eigenvectors 38:04
Part III: Linear Algebra, Lec 7: Dot Products 41:26
Part III: Linear Algebra, Lec 8: Orthogonal Functions 34:30
Nonlinear Finite Element Analysis by Klaus-Jürgen Bathe
# automatic playing for the 22 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年6月23日
MIT Nonlinear Finite Element Analysis
Course Description: Finite element analysis is now widely used for solving complex static and dynamic problems encountered in engineering and the sciences. In these two video courses, Professor K. J. Bathe, a researcher of world renown in the field of finite element analysis, teaches the basic principles used for effective finite element analysis, describes the general assumptions, and discusses the implementation of finite element procedures for linear and nonlinear analyses.
These videos were produced in 1982 and 1986 by the MIT Center for Advanced Engineering Study.
View the complete course: http://ocw.mit.edu/RES2-002S10
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
Instructor: Klaus-Jürgen Bathe
Lec 1 | Finite Element Procedures for Solids and Structures, Nonlinear Analysis 45:00
Lec 2 1:05:56
Lec 3 1:18:35
Lec 4 48:38
Lec 5 50:34
Lec 6 44:28
Lec 7 53:40
Lec 8 32:20
Lec 9 35:33
Lec 10 1:04:47
Lec 11 44:50
Lec 12 45:21
Lec 13 47:54
Lec 14 1:22:47
Lec 15 38:42
Lec 16 47:45
Lec 17 1:11:31
Lec 18 47:41
Lec 19 50:22
Lec 20 1:28:01
Lec 21 36:37
Lec 22 31:22
source: MIT OpenCourseWare Last updated on 2014年6月23日
MIT Nonlinear Finite Element Analysis
Course Description: Finite element analysis is now widely used for solving complex static and dynamic problems encountered in engineering and the sciences. In these two video courses, Professor K. J. Bathe, a researcher of world renown in the field of finite element analysis, teaches the basic principles used for effective finite element analysis, describes the general assumptions, and discusses the implementation of finite element procedures for linear and nonlinear analyses.
These videos were produced in 1982 and 1986 by the MIT Center for Advanced Engineering Study.
View the complete course: http://ocw.mit.edu/RES2-002S10
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
Instructor: Klaus-Jürgen Bathe
Lec 1 | Finite Element Procedures for Solids and Structures, Nonlinear Analysis 45:00
Lec 2 1:05:56
Lec 3 1:18:35
Lec 4 48:38
Lec 5 50:34
Lec 6 44:28
Lec 7 53:40
Lec 8 32:20
Lec 9 35:33
Lec 10 1:04:47
Lec 11 44:50
Lec 12 45:21
Lec 13 47:54
Lec 14 1:22:47
Lec 15 38:42
Lec 16 47:45
Lec 17 1:11:31
Lec 18 47:41
Lec 19 50:22
Lec 20 1:28:01
Lec 21 36:37
Lec 22 31:22
Linear Finite Element Analysis by Klaus-Jürgen Bathe
# automatic playing for the 12 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Linear Finite Element Analysis
Course Description: Finite element analysis is now widely used for solving complex static and dynamic problems encountered in engineering and the sciences. In these two video courses, Professor K. J. Bathe, a researcher of world renown in the field of finite element analysis, teaches the basic principles used for effective finite element analysis, describes the general assumptions, and discusses the implementation of finite element procedures for linear and nonlinear analyses.
These videos were produced in 1982 and 1986 by the MIT Center for Advanced Engineering Study.
View the complete course: http://ocw.mit.edu/RES2-002S10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 | Finite Element Procedures for Solids and Structures, Linear Analysis 45:29
Lec 2 58:56
Lec 3 57:08
Lec 4 56:06
Lec 5 55:56
Lec 6 56:14
Lec 7 51:50
Lec 8 46:02
Lec 9 59:16
Lec 10 55:42
Lec 11 47:22
Lec 12 57:34
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT Linear Finite Element Analysis
Course Description: Finite element analysis is now widely used for solving complex static and dynamic problems encountered in engineering and the sciences. In these two video courses, Professor K. J. Bathe, a researcher of world renown in the field of finite element analysis, teaches the basic principles used for effective finite element analysis, describes the general assumptions, and discusses the implementation of finite element procedures for linear and nonlinear analyses.
These videos were produced in 1982 and 1986 by the MIT Center for Advanced Engineering Study.
View the complete course: http://ocw.mit.edu/RES2-002S10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 | Finite Element Procedures for Solids and Structures, Linear Analysis 45:29
Lec 2 58:56
Lec 3 57:08
Lec 4 56:06
Lec 5 55:56
Lec 6 56:14
Lec 7 51:50
Lec 8 46:02
Lec 9 59:16
Lec 10 55:42
Lec 11 47:22
Lec 12 57:34
Introduction to Computer Science and Programming (Spring 2011) by John Guttag at MIT
# click the upper-left corner to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.00SC Introduction to Computer Science and Programming
Collection of 26 lectures given during the Spring 2011 semester of 6.00, Introduction to Computer Science and Programming. This course covers introductory computer science methods and topics. All programming assignments use Python.
View the complete course: http://ocw.mit.edu/6-00SCS11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 41:28
Lec 2 49:49
Rec 1 52:29
Lec 3 47:56
Lec 4 50:18
Rec 2 57:42
Lec 5 50:59
Lec 6 49:24
Rec 3 50:23
Lec 7 49:58
Rec 4 50:24
Lec 8 50:10
Lec 9 48:08
Lec 10 44:57
Lec 11 49:37
Rec 5 53:38
Lec 12 50:24
Optional Recitation 42:12
Lec 13 42:49
Lec 14 50:52
Lec 15 51:31
Rec 6 53:28
Lec 16 49:44
Lec 17 51:00
Rec 7 47:27
Lec 18 49:42
Lec 19 49:43
Quiz 2 Review Session 2:04:45
Lec 20 49:09
Rec 8 50:49
Lec 21 50:51
Lec 22 48:54
Rec 9 | 49:58
Lec 23 53:41
Lec 24 49:36
Rec 10 38:30
Lec 25 52:39
Lec 26 50:05
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.00SC Introduction to Computer Science and Programming
Collection of 26 lectures given during the Spring 2011 semester of 6.00, Introduction to Computer Science and Programming. This course covers introductory computer science methods and topics. All programming assignments use Python.
View the complete course: http://ocw.mit.edu/6-00SCS11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 41:28
Lec 2 49:49
Rec 1 52:29
Lec 3 47:56
Lec 4 50:18
Rec 2 57:42
Lec 5 50:59
Lec 6 49:24
Rec 3 50:23
Lec 7 49:58
Rec 4 50:24
Lec 8 50:10
Lec 9 48:08
Lec 10 44:57
Lec 11 49:37
Rec 5 53:38
Lec 12 50:24
Optional Recitation 42:12
Lec 13 42:49
Lec 14 50:52
Lec 15 51:31
Rec 6 53:28
Lec 16 49:44
Lec 17 51:00
Rec 7 47:27
Lec 18 49:42
Lec 19 49:43
Quiz 2 Review Session 2:04:45
Lec 20 49:09
Rec 8 50:49
Lec 21 50:51
Lec 22 48:54
Rec 9 | 49:58
Lec 23 53:41
Lec 24 49:36
Rec 10 38:30
Lec 25 52:39
Lec 26 50:05
Mathematics for Computer Science (Fall 2010) by Tom Leighton at MIT
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.042J Mathematics for Computer Science, Fall 2010
Instructor(s): Tom Leighton, Marten van Dijk
This course covers elementary discrete mathematics. Mathematical definitions and proofs are emphasized. Topics include formal logic, induction, graph theory, asymptotic notation and growth of functions, counting principles, and discrete probability.
View the complete course: http://ocw.mit.edu/6-042JF10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction and Proofs 44:10
Lec 2 Induction 1:19:25
Lec 3 Strong Induction 1:22:00
Lec 4 Number Theory I 1:20:25
Lec 5 Number Theory II 1:18:45
Lec 6 Graph Theory and Coloring 1:22:51
Lec 7 Matching Problems 1:22:41
Lec 8 Graph Theory II Minimum Spanning Trees 1:23:26
Lec 9 Communication Networks 1:23:26
Lec 10 Graph Theory III 1:22:50
Lec 11 Relations, Partial Orders, and Scheduling 1:04:00
Lec 12 Sums 1:18:22
Lec 13 Sums and Asymptotics 1:23:40
Lec 14 Divide and Conquer Recurrences 1:22:46
Lec 15 Linear Recurrences 1:18:20
Lec 16 Counting Rules I 1:20:03
Lec 17 Counting Rules II 1:25:24
Lec 18 Probability Introduction 1:23:56
Lec 19 Conditional Probability 1:21:46
Lec 20 Independence 1:22:02
Lec 21 Random Variables 1:23:00
Lec 22 Expectation I 1:23:54
Lec 23 Expectation II 1:23:44
Lec 24 Large Deviations 1:23:23
Lec 25 Random Walks 1:17:53
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.042J Mathematics for Computer Science, Fall 2010
Instructor(s): Tom Leighton, Marten van Dijk
This course covers elementary discrete mathematics. Mathematical definitions and proofs are emphasized. Topics include formal logic, induction, graph theory, asymptotic notation and growth of functions, counting principles, and discrete probability.
View the complete course: http://ocw.mit.edu/6-042JF10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction and Proofs 44:10
Lec 2 Induction 1:19:25
Lec 3 Strong Induction 1:22:00
Lec 4 Number Theory I 1:20:25
Lec 5 Number Theory II 1:18:45
Lec 6 Graph Theory and Coloring 1:22:51
Lec 7 Matching Problems 1:22:41
Lec 8 Graph Theory II Minimum Spanning Trees 1:23:26
Lec 9 Communication Networks 1:23:26
Lec 10 Graph Theory III 1:22:50
Lec 11 Relations, Partial Orders, and Scheduling 1:04:00
Lec 12 Sums 1:18:22
Lec 13 Sums and Asymptotics 1:23:40
Lec 14 Divide and Conquer Recurrences 1:22:46
Lec 15 Linear Recurrences 1:18:20
Lec 16 Counting Rules I 1:20:03
Lec 17 Counting Rules II 1:25:24
Lec 18 Probability Introduction 1:23:56
Lec 19 Conditional Probability 1:21:46
Lec 20 Independence 1:22:02
Lec 21 Random Variables 1:23:00
Lec 22 Expectation I 1:23:54
Lec 23 Expectation II 1:23:44
Lec 24 Large Deviations 1:23:23
Lec 25 Random Walks 1:17:53
Darwin and Design (Fall 2010) by James Paradis at MIT
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年6月25日
MIT 21L.448J Darwin and Design, Fall 2010
Course Description: Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.
View the complete course: http://ocw.mit.edu/21L-448JF10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 53:07
Lec 2 56:22
Lec 3 59:08
Lec 4 49:35
Lec 5 1:02:07
Lec 6 56:25
Lec 7 1:03:36
Lec 8 56:03
Lec 9 58:08
Lec 10 46:17
Lec 11 54:06
Lec 12 54:31
Lec 13 52:31
Lec 14 49:44
Lec 15 52:05
Lec 16 51:19
Lec 17 54:07
Lec 18 56:59
Lec 20 55:16
Lec 21 52:21
Lec 22 59:18
source: MIT OpenCourseWare Last updated on 2014年6月25日
MIT 21L.448J Darwin and Design, Fall 2010
Course Description: Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.
View the complete course: http://ocw.mit.edu/21L-448JF10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 53:07
Lec 2 56:22
Lec 3 59:08
Lec 4 49:35
Lec 5 1:02:07
Lec 6 56:25
Lec 7 1:03:36
Lec 8 56:03
Lec 9 58:08
Lec 10 46:17
Lec 11 54:06
Lec 12 54:31
Lec 13 52:31
Lec 14 49:44
Lec 15 52:05
Lec 16 51:19
Lec 17 54:07
Lec 18 56:59
Lec 20 55:16
Lec 21 52:21
Lec 22 59:18
Jon Gruber: Principles of Microeconomics (Fall 2010 at MIT)
# automatic playing for the 38 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 14.01SC Principles of Microeconomics
View the complete course: http://ocw.mit.edu/14-01SCF10
This course includes a full set of lecture videos and a selection of problem solving videos. In the lecture videos, Professor Jonathan Gruber covers the principles of microeconomics conceptually, mathematically, and graphically, giving students a holistic understanding of the subject matter. He then moves on to more advanced topics in microeconomics to provide further insight into its many different applications.
In the problem solving videos, a teaching assistant demonstrates their approach to questions from the problem set. The problems selected by the TA cover a range of microeconomics topics and problem solving techniques that students need to master to successfully complete this course.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction to Microeconomics 34:14
Lec 2 Applying Supply and Demand 49:07
Lec 3 Elasticity 47:58
Lec 4 Preferences and Utility 48:10
Lec 5 Budget Constraints 46:14
Lec 6 Deriving Demand Curves 47:22
Lec 7 Applying Consumer Theory Labor Supply 50:26
Lec 8a Applying Consumer Theory Child Labor 13:38
Lec 8 Introduction to Producer Theory 37:22
Lec 9 Productivity and Costs 47:31
Lec 10 Competition I 49:00
Lec 11 Competition II 50:07
Lec 12 Competition III 45:06
Lec 13 Welfare economics 47:08
Lec 14 Monopoly 46:57
Lec 15 Monopoly II 48:25
Lec 16 Oligopoly 50:05
Lec 17 Oligopoly II 47:22
Lec 18 Factor Markets 46:38
Lec 19 International Trade 45:43
Lec 20 Uncertainty 48:01
Lec 21 Capital Supply and Markets I 48:22
Lec 22 Capital Supply and Markets II 47:31
Lec 23 Equity and Efficiency 49:37
Lec 24 Government Redistribution Policy 49:12
Lec 25 U.S. Social Insurance Programs 49:45
Lec 26 Healthcare Economics 39:20
Introduction 0:54
Problem Set 1, Problem #3 15:20
Problem Set 1, Problem #4 15:15
Problem Set 2, Problem #4 17:24
Problem Set 3, Problem #5 24:34
Problem Set 4, Problem #3 15:01
Problem Set 5, Problem #4e-h 14:17
Problem Set 6, Problem #3 16:37
Problem Set 6, Problem #4 17:44
Problem Set 7, Problem #2a-e 16:33
Problem Set 8, Problem #2a-b 15:59
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 14.01SC Principles of Microeconomics
View the complete course: http://ocw.mit.edu/14-01SCF10
This course includes a full set of lecture videos and a selection of problem solving videos. In the lecture videos, Professor Jonathan Gruber covers the principles of microeconomics conceptually, mathematically, and graphically, giving students a holistic understanding of the subject matter. He then moves on to more advanced topics in microeconomics to provide further insight into its many different applications.
In the problem solving videos, a teaching assistant demonstrates their approach to questions from the problem set. The problems selected by the TA cover a range of microeconomics topics and problem solving techniques that students need to master to successfully complete this course.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction to Microeconomics 34:14
Lec 2 Applying Supply and Demand 49:07
Lec 3 Elasticity 47:58
Lec 4 Preferences and Utility 48:10
Lec 5 Budget Constraints 46:14
Lec 6 Deriving Demand Curves 47:22
Lec 7 Applying Consumer Theory Labor Supply 50:26
Lec 8a Applying Consumer Theory Child Labor 13:38
Lec 8 Introduction to Producer Theory 37:22
Lec 9 Productivity and Costs 47:31
Lec 10 Competition I 49:00
Lec 11 Competition II 50:07
Lec 12 Competition III 45:06
Lec 13 Welfare economics 47:08
Lec 14 Monopoly 46:57
Lec 15 Monopoly II 48:25
Lec 16 Oligopoly 50:05
Lec 17 Oligopoly II 47:22
Lec 18 Factor Markets 46:38
Lec 19 International Trade 45:43
Lec 20 Uncertainty 48:01
Lec 21 Capital Supply and Markets I 48:22
Lec 22 Capital Supply and Markets II 47:31
Lec 23 Equity and Efficiency 49:37
Lec 24 Government Redistribution Policy 49:12
Lec 25 U.S. Social Insurance Programs 49:45
Lec 26 Healthcare Economics 39:20
Introduction 0:54
Problem Set 1, Problem #3 15:20
Problem Set 1, Problem #4 15:15
Problem Set 2, Problem #4 17:24
Problem Set 3, Problem #5 24:34
Problem Set 4, Problem #3 15:01
Problem Set 5, Problem #4e-h 14:17
Problem Set 6, Problem #3 16:37
Problem Set 6, Problem #4 17:44
Problem Set 7, Problem #2a-e 16:33
Problem Set 8, Problem #2a-b 15:59
Introduction to Psychology by John Gabrieli (Fall 2011 at MIT)
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 9.00SC Introduction to Psychology, Fall 2011
View the complete course: http://ocw.mit.edu/9-00SCS11
Introduction to Psychology is a survey of the scientific study of human nature, including how the mind works, and how the brain supports the mind. Topics include the mental and neural bases of perception, emotion, learning, memory, cognition, child development, personality, psychopathology, and social interaction.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction 49:44
Lec 2 Science and Research 1:11:16
Lec 3 Brain I - Structure and Functions 54:19
Lec 4 Brain II - Methods of Research 1:11:01
Lec 5 Vision I 40:59
Lec 6 Vision II 56:20
Lec 7 Attention 42:20
Lec 8 Consciousness 49:50
Lec 9 Learning 46:43
Lec 10 Memory I 55:51
Lec 11 Memory II - Amnesia and Memory Systems 58:08
Lec 12 Language 53:17
Lec 13 Thinking 1:07:25
Lec 14 Intelligence 1:12:00
Lec 15 Emotion & Motivation 1:00:08
Lec 16 Personality 1:00:25
Lec 17 Child Development 33:25
Lec 18 Adult Development 1:11:53
Lec 19 Stress 1:11:10
Lec 20 Psychopathology I 51:07
Lec 21 Psychopathology II 59:47
Lec 22 Social Psychology I 1:06:43
Lec 23 Social Psychology II 1:07:49
Lec 24 Conclusions - Evolutionary Psychology, Happiness 49:51
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 9.00SC Introduction to Psychology, Fall 2011
View the complete course: http://ocw.mit.edu/9-00SCS11
Introduction to Psychology is a survey of the scientific study of human nature, including how the mind works, and how the brain supports the mind. Topics include the mental and neural bases of perception, emotion, learning, memory, cognition, child development, personality, psychopathology, and social interaction.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Lec 1 Introduction 49:44
Lec 2 Science and Research 1:11:16
Lec 3 Brain I - Structure and Functions 54:19
Lec 4 Brain II - Methods of Research 1:11:01
Lec 5 Vision I 40:59
Lec 6 Vision II 56:20
Lec 7 Attention 42:20
Lec 8 Consciousness 49:50
Lec 9 Learning 46:43
Lec 10 Memory I 55:51
Lec 11 Memory II - Amnesia and Memory Systems 58:08
Lec 12 Language 53:17
Lec 13 Thinking 1:07:25
Lec 14 Intelligence 1:12:00
Lec 15 Emotion & Motivation 1:00:08
Lec 16 Personality 1:00:25
Lec 17 Child Development 33:25
Lec 18 Adult Development 1:11:53
Lec 19 Stress 1:11:10
Lec 20 Psychopathology I 51:07
Lec 21 Psychopathology II 59:47
Lec 22 Social Psychology I 1:06:43
Lec 23 Social Psychology II 1:07:49
Lec 24 Conclusions - Evolutionary Psychology, Happiness 49:51
John Tsitsiklis: Probabilistic Systems Analysis and Applied Probability (Fall 2010, MIT)
# automatic playing for the 25 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010
View the complete course: http://ocw.mit.edu/6-041F10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Probability Models and Axioms 51:11
2. Conditioning and Bayes' Rule 51:11
3. Independence 46:30
4. Counting 51:35
5. Discrete Random Variables I 50:35
6. Discrete Random Variables II 50:53
7. Discrete Random Variables III 50:42
8. Continuous Random Variables 50:29
9. Multiple Continuous Random Variables 50:51
10. Continuous Bayes' Rule; Derived Distributions 48:53
11. Derived Distributions (ctd.); Covariance 51:55
12. Iterated Expectations 47:54
13. Bernoulli Process 50:58
14. Poisson Process I 52:44
15. Poisson Process II 49:28
16. Markov Chains I
17. Markov Chains II 51:25
18. Markov Chains III 51:50
19. Weak Law of Large Numbers 50:13
20. Central Limit Theorem 51:23
21. Bayesian Statistical Inference I 48:50
22. Bayesian Statistical Inference II 52:16
23. Classical Statistical Inference I 49:32
24. Classical Inference II 51:50
25. Classical Inference III 52:07
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010
View the complete course: http://ocw.mit.edu/6-041F10
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Probability Models and Axioms 51:11
2. Conditioning and Bayes' Rule 51:11
3. Independence 46:30
4. Counting 51:35
5. Discrete Random Variables I 50:35
6. Discrete Random Variables II 50:53
7. Discrete Random Variables III 50:42
8. Continuous Random Variables 50:29
9. Multiple Continuous Random Variables 50:51
10. Continuous Bayes' Rule; Derived Distributions 48:53
11. Derived Distributions (ctd.); Covariance 51:55
12. Iterated Expectations 47:54
13. Bernoulli Process 50:58
14. Poisson Process I 52:44
15. Poisson Process II 49:28
16. Markov Chains I
17. Markov Chains II 51:25
18. Markov Chains III 51:50
19. Weak Law of Large Numbers 50:13
20. Central Limit Theorem 51:23
21. Bayesian Statistical Inference I 48:50
22. Bayesian Statistical Inference II 52:16
23. Classical Statistical Inference I 49:32
24. Classical Inference II 51:50
25. Classical Inference III 52:07
Health Information Systems, Spring 2012 at MIT
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年6月29日
MIT HST.S14 Health Information Systems, Spring 2012
Instructor(s): Leo Anthony Celi, Peter Szolovits, Hamish Fraser, Ken Paik
View the complete course at: http://ocw.mit.edu/HST-S14S12
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Translational Research and Advocacy 1:20:23
2. Design and Impact of Health Information Systems 1:11:49
3. Overview of Quality Improvement 51:03
4. Health Systems Research 1:02:35
5. Process Improvement Theory and Application 1:12:42
6. Innovation and Adoption of New Practices 44:38
7. WHO Safe Surgery and Safe Childbirth Checklists 1:03:34
8. What is Quality and Why Should We Measure It? 39:12
9. Patient Safety in Resource-Poor Settings 25:36
10. Organizational Change: Positive Deviance 48:47
11. The Millenium Global Village-Network 1:09:26
12. A Perspective on Monitoring and Evaluation 1:01:15
source: MIT OpenCourseWare Last updated on 2014年6月29日
MIT HST.S14 Health Information Systems, Spring 2012
Instructor(s): Leo Anthony Celi, Peter Szolovits, Hamish Fraser, Ken Paik
View the complete course at: http://ocw.mit.edu/HST-S14S12
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Translational Research and Advocacy 1:20:23
2. Design and Impact of Health Information Systems 1:11:49
3. Overview of Quality Improvement 51:03
4. Health Systems Research 1:02:35
5. Process Improvement Theory and Application 1:12:42
6. Innovation and Adoption of New Practices 44:38
7. WHO Safe Surgery and Safe Childbirth Checklists 1:03:34
8. What is Quality and Why Should We Measure It? 39:12
9. Patient Safety in Resource-Poor Settings 25:36
10. Organizational Change: Positive Deviance 48:47
11. The Millenium Global Village-Network 1:09:26
12. A Perspective on Monitoring and Evaluation 1:01:15
The Challenge of World Poverty, Spring 2011
# automatic playing for the 22 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 14.73 The Challenge of World Poverty, Spring 2011
Instructors: Prof. Esther Duflo and Prof. Abhijit Banerjee
View the complete course: http://ocw.mit.edu/14-73S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction 34:36
2. What is a Poverty Trap? 1:23:50
3. Social Experiments: Why and How? 1:00:42
5. Is There a Nutrition-Based Poverty Trap? 1:16:40
6. Nutrition: The Hidden Traps 1:20:26
8. Health: Low Hanging Fruit? 1:20:27
9. Education: Setting the Stage 52:45
10. Is It Possible to Deliver Quality Education to the Poor-The Pratham-JPAL Partnership 1:08:24
11. Education: The Man Made Trap 1:14:05
12. (Somewhat) Un-Orthodox Findings on the Family 1:14:05
13. How Do Families Decide? 1:18:58
14. Gender Discrimination 1:06:02
15. Risk and Insurance 1:19:11
16. Insurance 1:12:11
17. The (Not So Simple) Economics of Lending to the Poor 1:18:19
19. The Promise and Perils of Microfinance 33:44
20. Savings 1:14:29
21. Savings 2 1:12:20
22. Entrepreneurs and Workers 1:16:14
24. Policies, Politics: Can Evidence Play a Role in the Fight Against Poverty? 1:04:15
25. Policies, Politics: Can Evidence Play a Role in the Fight Against Poverty?, cont. 1:23:46
26. Five Thoughts in Place of a Sweeing Conclusion 47:46
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 14.73 The Challenge of World Poverty, Spring 2011
Instructors: Prof. Esther Duflo and Prof. Abhijit Banerjee
View the complete course: http://ocw.mit.edu/14-73S11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Introduction 34:36
2. What is a Poverty Trap? 1:23:50
3. Social Experiments: Why and How? 1:00:42
5. Is There a Nutrition-Based Poverty Trap? 1:16:40
6. Nutrition: The Hidden Traps 1:20:26
8. Health: Low Hanging Fruit? 1:20:27
9. Education: Setting the Stage 52:45
10. Is It Possible to Deliver Quality Education to the Poor-The Pratham-JPAL Partnership 1:08:24
11. Education: The Man Made Trap 1:14:05
12. (Somewhat) Un-Orthodox Findings on the Family 1:14:05
13. How Do Families Decide? 1:18:58
14. Gender Discrimination 1:06:02
15. Risk and Insurance 1:19:11
16. Insurance 1:12:11
17. The (Not So Simple) Economics of Lending to the Poor 1:18:19
19. The Promise and Perils of Microfinance 33:44
20. Savings 1:14:29
21. Savings 2 1:12:20
22. Entrepreneurs and Workers 1:16:14
24. Policies, Politics: Can Evidence Play a Role in the Fight Against Poverty? 1:04:15
25. Policies, Politics: Can Evidence Play a Role in the Fight Against Poverty?, cont. 1:23:46
26. Five Thoughts in Place of a Sweeing Conclusion 47:46
Introduction to Algorithms (Fall 2011) by Srini Devadas at MIT
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source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.006 Introduction to Algorithms, Fall 2011
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
View the complete course: http://ocw.mit.edu/6-006F11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Algorithmic Thinking, Peak Finding 53:22
2. Models of Computation, Document Distance 48:52
3. Insertion Sort, Merge Sort 51:20
4. Heaps and Heap Sort 52:32
5. Binary Search Trees, BST Sort 52:40
6. AVL Trees, AVL Sort 51:59
7. Counting Sort, Radix Sort, Lower Bounds for Sorting 52:09
8. Hashing with Chaining 51:16
9. Table Doubling, Karp-Rabin 52:47
10. Open Addressing, Cryptographic Hashing 50:55
11. Integer Arithmetic, Karatsuba Multiplication 47:24
12. Square Roots, Newton's Method 51:17
13. Breadth-First Search (BFS) 50:48
14. Depth-First Search (DFS), Topological Sort 50:31
15. Single-Source Shortest Paths Problem 53:15
16. Dijkstra 51:26
17. Bellman-Ford 48:51
18. Speeding up Dijkstra 53:16
19. Dynamic Programming I: Fibonacci, Shortest Paths 51:47
20. Dynamic Programming II: Text Justification, Blackjack 52:12
21. DP III: Parenthesization, Edit Distance, Knapsack 52:41
22. DP IV: Guitar Fingering, Tetris, Super Mario Bros. 49:20
23. Computational Complexity 51:12
24. Topics in Algorithms Research 46:46
R1. Asymptotic Complexity, Peak Finding 53:50
R2. Python Cost Model, Document Distance 52:21
R3. Document Distance, Insertion and Merge Sort 54:11
R5. Recursion Trees, Binary Search Trees 59:16
R6. AVL Trees 53:28
R7. Comparison Sort, Counting and Radix Sort 51:09
R8. Simulation Algorithms 55:39
R9. Rolling Hashes, Amortized Analysis 1:01:01
Recitation 9b: DNA Sequence Matching 57:28
R10. Quiz 1 Review 54:49
R11. Principles of Algorithm Design 58:26
R12. Karatsuba Multiplication, Newton's Method 53:08
R13. Breadth-First Search (BFS) 54:53
R14. Depth-First Search (DFS) 53:39
R15. Shortest Paths 56:31
R16. Rubik's Cube, StarCraft Zero 54:36
R18. Quiz 2 Review 1:05:30
R19. Dynamic Programming: Crazy Eights, Shortest Path 52:47
R20. Dynamic Programming: Blackjack 52:58
R22. Dynamic Programming: Dance Dance Revolution 53:16
R21. Dynamic Programming: Knapsack Problem 1:09:12
R23. Computational Complexity 47:14
R24. Final Exam Review 51:44
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 6.006 Introduction to Algorithms, Fall 2011
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
View the complete course: http://ocw.mit.edu/6-006F11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Algorithmic Thinking, Peak Finding 53:22
2. Models of Computation, Document Distance 48:52
3. Insertion Sort, Merge Sort 51:20
4. Heaps and Heap Sort 52:32
5. Binary Search Trees, BST Sort 52:40
6. AVL Trees, AVL Sort 51:59
7. Counting Sort, Radix Sort, Lower Bounds for Sorting 52:09
8. Hashing with Chaining 51:16
9. Table Doubling, Karp-Rabin 52:47
10. Open Addressing, Cryptographic Hashing 50:55
11. Integer Arithmetic, Karatsuba Multiplication 47:24
12. Square Roots, Newton's Method 51:17
13. Breadth-First Search (BFS) 50:48
14. Depth-First Search (DFS), Topological Sort 50:31
15. Single-Source Shortest Paths Problem 53:15
16. Dijkstra 51:26
17. Bellman-Ford 48:51
18. Speeding up Dijkstra 53:16
19. Dynamic Programming I: Fibonacci, Shortest Paths 51:47
20. Dynamic Programming II: Text Justification, Blackjack 52:12
21. DP III: Parenthesization, Edit Distance, Knapsack 52:41
22. DP IV: Guitar Fingering, Tetris, Super Mario Bros. 49:20
23. Computational Complexity 51:12
24. Topics in Algorithms Research 46:46
R1. Asymptotic Complexity, Peak Finding 53:50
R2. Python Cost Model, Document Distance 52:21
R3. Document Distance, Insertion and Merge Sort 54:11
R5. Recursion Trees, Binary Search Trees 59:16
R6. AVL Trees 53:28
R7. Comparison Sort, Counting and Radix Sort 51:09
R8. Simulation Algorithms 55:39
R9. Rolling Hashes, Amortized Analysis 1:01:01
Recitation 9b: DNA Sequence Matching 57:28
R10. Quiz 1 Review 54:49
R11. Principles of Algorithm Design 58:26
R12. Karatsuba Multiplication, Newton's Method 53:08
R13. Breadth-First Search (BFS) 54:53
R14. Depth-First Search (DFS) 53:39
R15. Shortest Paths 56:31
R16. Rubik's Cube, StarCraft Zero 54:36
R18. Quiz 2 Review 1:05:30
R19. Dynamic Programming: Crazy Eights, Shortest Path 52:47
R20. Dynamic Programming: Blackjack 52:58
R22. Dynamic Programming: Dance Dance Revolution 53:16
R21. Dynamic Programming: Knapsack Problem 1:09:12
R23. Computational Complexity 47:14
R24. Final Exam Review 51:44
Nano-to-Micro Transport Processes by Gang Chen (Spring 2012)
# automatic playing for the 25 videos (click the up-left corner for the list)
source: MIT OpenCourseWare Last updated on 2014年6月23日
MIT 2.57 Nano-to-Micro Transport Processes, Spring 2012
View the complete course: http://ocw.mit.edu/2-57S12
This course aims at a fundamental understanding of descriptive tools for energy and heat transport processes, from nanoscale to macroscale. Student will further learn the applications in nanotechnology and microtechnology.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Intro to Nanotechnology, Nanoscale Transport Phenomena 1:18:11
2. Characteristic Time and Length, Simple Kinetic Theory 1:20:35
3. Schrödinger Equation and Material Waves 1:20:35
4. Solutions to Schrödinger Equation, Energy Quantization 1:22:12
5. Electronic Levels in One-Dimensional Lattice Chain 1:20:06
6. Crystal Bonding & Electronic Energy Levels in Crystals 1:20:18
7. Phonon Energy Levels in Crystal and Crystal Structures 1:22:02
8. Density of States and Statistical Distributions 1:21:20
9. Specific Heat and Planck's Law 1:18:41
10. Fundamental of Statistical Thermodynamics 1:18:20
11. Energy Transfer by Waves: Plane Waves 1:21:35
12. EM Waves: Reflection at a Single Interface 1:21:28
13. EM Wave Propagation Through Thin Films & Multilayers 1:15:25
14. Wave Phenomena and Landauer Formalism 1:21:32
15. Particle Description, Liouville & Boltzmann Equations 1:19:20
16. Fermi Golden Rule and Relaxation Time Approximation 1:20:51
17. Solutions to Boltzmann Equation: Diffusion Laws 1:21:58
18. Electron Transport and Thermoelectric Effects 1:22:25
19. Classical Size Effects, Parallel Direction 1:20:33
20. Classical Size Effects, Perpendicular Direction 1:20:03
21. Slip Condition, Coupled Energy Transport & Conversion 1:21:02
22. PN Junction, Diode and Photovoltaic Cells 1:20:41
23. Liquids: Brownian Motion and Forces in Liquids 1:23:27
24. Electrical Double Layer, Size Effects in Phase Change 1:17:38
25. Statistical Foundation for Molecular Dynamics Simulation 1:24:06
source: MIT OpenCourseWare Last updated on 2014年6月23日
MIT 2.57 Nano-to-Micro Transport Processes, Spring 2012
View the complete course: http://ocw.mit.edu/2-57S12
This course aims at a fundamental understanding of descriptive tools for energy and heat transport processes, from nanoscale to macroscale. Student will further learn the applications in nanotechnology and microtechnology.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1. Intro to Nanotechnology, Nanoscale Transport Phenomena 1:18:11
2. Characteristic Time and Length, Simple Kinetic Theory 1:20:35
3. Schrödinger Equation and Material Waves 1:20:35
4. Solutions to Schrödinger Equation, Energy Quantization 1:22:12
5. Electronic Levels in One-Dimensional Lattice Chain 1:20:06
6. Crystal Bonding & Electronic Energy Levels in Crystals 1:20:18
7. Phonon Energy Levels in Crystal and Crystal Structures 1:22:02
8. Density of States and Statistical Distributions 1:21:20
9. Specific Heat and Planck's Law 1:18:41
10. Fundamental of Statistical Thermodynamics 1:18:20
11. Energy Transfer by Waves: Plane Waves 1:21:35
12. EM Waves: Reflection at a Single Interface 1:21:28
13. EM Wave Propagation Through Thin Films & Multilayers 1:15:25
14. Wave Phenomena and Landauer Formalism 1:21:32
15. Particle Description, Liouville & Boltzmann Equations 1:19:20
16. Fermi Golden Rule and Relaxation Time Approximation 1:20:51
17. Solutions to Boltzmann Equation: Diffusion Laws 1:21:58
18. Electron Transport and Thermoelectric Effects 1:22:25
19. Classical Size Effects, Parallel Direction 1:20:33
20. Classical Size Effects, Perpendicular Direction 1:20:03
21. Slip Condition, Coupled Energy Transport & Conversion 1:21:02
22. PN Junction, Diode and Photovoltaic Cells 1:20:41
23. Liquids: Brownian Motion and Forces in Liquids 1:23:27
24. Electrical Double Layer, Size Effects in Phase Change 1:17:38
25. Statistical Foundation for Molecular Dynamics Simulation 1:24:06
Finance Theory I by Andrew Lo (Fall 2008)
# click the upper-left icon to select videos from the playlist
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
This course provides a rigorous introduction to the fundamentals of modern financial analysis and applications to business challenges in valuation, risk analysis, corporate investment decisions, and basic security analysis and investment management. The four major sections of the course are: (A) an introduction to the financial system, the financial challenges firms and households face, and the principles of modern finance in tackling these challenges; (B) valuation of stocks, bonds, forwards, futures, and options; (C) methods for incorporating risk analysis into valuation models, including portfolio theory, mean-variance optimization, and the Capital Asset Pricing Model; and (D) applications to corporate financial decisions, including capital budgeting and real options.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Ses 1: Introduction and Course Overview 1:07:13
Ses 2: Present Value Relations I 1:15:56
Ses 3: Present Value Relations II 1:20:15
Ses 4: Present Value Relations III & Fixed-Income Securities I 1:11:57
Ses 5: Fixed-Income Securities II 1:19:19
Ses 6: Fixed-Income Securities III 1:19:54
Ses 7: Fixed-Income Securities IV 1:15:57
Ses 8: Equities 1:15:31
Ses 9: Forward and Futures Contracts I 1:19:13
Ses 10: Forward and Futures Contracts II & Options I 1:19:50
Ses 11: Options II 58:53
Ses 12: Options III & Risk and Return I 1:07:01
Ses 13: Risk and Return II & Portfolio Theory I 1:18:37
Ses 14: Portfolio Theory II 1:20:40
Ses 15: Portfolio Theory III & The CAPM and APT I 1:18:34
Ses 16: The CAPM and APT II 1:15:25
Ses 17: The CAPM and APT III & Capital Budgeting I 1:20:28
Ses 18: Capital Budgeting II & Efficient Markets I 1:19:50
Ses 19: Efficient Markets II 1:20:10
Ses 20: Efficient Markets III & Course Summary 54:25
source: MIT OpenCourseWare Last updated on 2014年7月2日
MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
This course provides a rigorous introduction to the fundamentals of modern financial analysis and applications to business challenges in valuation, risk analysis, corporate investment decisions, and basic security analysis and investment management. The four major sections of the course are: (A) an introduction to the financial system, the financial challenges firms and households face, and the principles of modern finance in tackling these challenges; (B) valuation of stocks, bonds, forwards, futures, and options; (C) methods for incorporating risk analysis into valuation models, including portfolio theory, mean-variance optimization, and the Capital Asset Pricing Model; and (D) applications to corporate financial decisions, including capital budgeting and real options.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Ses 1: Introduction and Course Overview 1:07:13
Ses 2: Present Value Relations I 1:15:56
Ses 3: Present Value Relations II 1:20:15
Ses 4: Present Value Relations III & Fixed-Income Securities I 1:11:57
Ses 5: Fixed-Income Securities II 1:19:19
Ses 6: Fixed-Income Securities III 1:19:54
Ses 7: Fixed-Income Securities IV 1:15:57
Ses 8: Equities 1:15:31
Ses 9: Forward and Futures Contracts I 1:19:13
Ses 10: Forward and Futures Contracts II & Options I 1:19:50
Ses 11: Options II 58:53
Ses 12: Options III & Risk and Return I 1:07:01
Ses 13: Risk and Return II & Portfolio Theory I 1:18:37
Ses 14: Portfolio Theory II 1:20:40
Ses 15: Portfolio Theory III & The CAPM and APT I 1:18:34
Ses 16: The CAPM and APT II 1:15:25
Ses 17: The CAPM and APT III & Capital Budgeting I 1:20:28
Ses 18: Capital Budgeting II & Efficient Markets I 1:19:50
Ses 19: Efficient Markets II 1:20:10
Ses 20: Efficient Markets III & Course Summary 54:25
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