2018-02-08
Philosophy of Biology by Kane B
source: Kane B
This playlist collects the various videos I have made on topics relating to philosophy of biology.
1 33:05 Adaptationism 1
2 33:28 Adaptationism 2
3 30:34 Fitness and Natural Selection
4 28:38 Biology of Race 1
5 29:15 Biology of Race 2
6 39:18 Evolutionary Psychology
7 27:17 The Evolutionary Theory of Rape 1
8 28:28 The Evolutionary Theory of Rape 2
9 21:22 Astrobiology - Planetary Habitability 1
10 21:27 Astrobiology - Planetary Habitability 2
11 42:41 Is there a shadow biosphere?
12 36:39 Philosophy of Medicine - What is Disease? 1
13 36:40 Philosophy of Medicine - What is Disease? 2
14 32:12 Philosophy of Mind - Animal Minds 1
15 33:51 Punctuated Equilibrium 1
16 25:50 Punctuated Equilibrium 2 - Objections 1
17 29:36 Punctuated Equilibrium 3 - Objections 2
18 34:22 Functions in Biology
19 38:39 Philosophy of Biology - The Species Problem 1
20 45:52 Philosophy of Biology - The Species Problem 2
21 27:07 Evolutionary Progress 1
22 26:59 Evolutionary Progress 2
23 38:26 History of Biology - Darwin 1
24 25:48 History of biology: the objections to Darwinism 1
25 32:20 History of biology: the objections to Darwinism 2
26 17:58 Environmental ethics and Sylvan's "Last Man" argument
27 33:04 Environmental Ethics - Nature Restoration
28 31:08 The Ethics of Terraforming 1
29 30:23 The Ethics of Terraforming 2
Philosophy of Science by Kane B
source: Kane B 2017年1月21日
This series is not based on any particular book, but "Understanding Philosophy of Science" by James Ladyman and "Theory and Reality" by Peter Godfrey-Smith are two introductions to philosophy of science that have been helpful to me, and are good places to go for further reading. I also recommend "Representing and Intervening" by Ian Hacking. This was written in 1983, so it's somewhat dated now, but it's worth reading because it emphasizes that philosophers of science need to attend to the details experimentation and scientific practice. Most philosophy of science until recently was focused on theory and other more "abstract" matters; Hacking provides an interesting alternative approach. (I hope in the course of this series to focus more on experiment and practice than is standard for an intro to philosophy of science, though that won't be for a few more videos.)
1 - Induction and Naive Inductivism 24:22 In this video, I outline the difference between inductive and deductive reasoning, and then examine the "naive inductivist" model of the scientific method, according to which science essentially involves collecting a large number of unbiased observations and then drawing inductive generalizations on the basis of these.
2 - The Hypothetico-Deductive Method 31:36
3 - Hume's Problem of Induction 27:00
4 - Goodman's Problem of Induction 32:27
5 - Falsificationism 35:13
6 - Objections to Falsificationism 32:10
7 - Scientific Revolutions 31:20
8 - Incommensurability 43:22
9 34:14 Research Programmes
10 26:04 Against Method 1
11 19:42 Against Method 2
12 31:29 Science and Democracy
13 36:38 The Experimenter's Regress
14 19:58 Scientific Explanation 1 - The Deductive-Nomological Model
15 22:28 Scientific Explanation 2 - Objections to the DN Model
16 19:43 Scientific Explanation 3 - The Causal-Mechanical Model
17 20:48 Scientific Explanation 4 - Objections to the CM Model
18 22:18 Scientific Explanation 5 - The Unification Model
19 25:20 Scientific Explanation 6 - Objections to the Unification Model
20 34:09 Scientific Progress
21 30:44 Is science contingent? 1
22 30:45 Is science contingent? 2
23 37:33 Metaphysics - Levels of Reality
The Film Experience (Fall 2007) by David Thorburn at MIT
# playlist: click the video's upper-left icon
source: MIT OpenCourseWare 2017年7月21日
MIT 21L.011 The Film Experience, Fall 2007
View the complete course: http://ocw.mit.edu/21L-011F07
Instructor: David Thorburn
This introduction to narrative film emphasizes the evolution of the film medium and the intrinsic artistic qualities of individual films. The selected lectures in this video collection cover early cinema & silent films, the 1970s, and neorealism.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
1 55:34 Introduction
2 1:06:04 Keaton
3 54:40 Chaplin
4 1:02:20 Chaplin, Part II (2007)
5 52:50 Film in the 1970s, Part I
6 56:38 Film in the 1970s, Part II
7 52:12 Italian Neorealism, Part I
8 51:05 Italian neorealism
source: MIT OpenCourseWare 2017年7月21日
MIT 21L.011 The Film Experience, Fall 2007
View the complete course: http://ocw.mit.edu/21L-011F07
Instructor: David Thorburn
This introduction to narrative film emphasizes the evolution of the film medium and the intrinsic artistic qualities of individual films. The selected lectures in this video collection cover early cinema & silent films, the 1970s, and neorealism.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
1 55:34 Introduction
2 1:06:04 Keaton
3 54:40 Chaplin
4 1:02:20 Chaplin, Part II (2007)
5 52:50 Film in the 1970s, Part I
6 56:38 Film in the 1970s, Part II
7 52:12 Italian Neorealism, Part I
8 51:05 Italian neorealism
Introduction to Computer Science and Programming in Python (Fall 2016) by Ana Bell at MIT
# playlist: click the video's upper-left icon
source: MIT OpenCourseWare 2017年2月15日
6.0001 Introduction to Computer Science and Programming in Python. Fall 2016
View the complete course: http://ocw.mit.edu/6-0001F16
Instructor: Dr. Ana Bell
6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1 43:06 1. What is Computation?
2 1:20 Shell vs. Editor
3 1:37 Python vs. Math
4 1:40 Bindings
5 43:31 2. Branching and Iteration
6 0:53 Strings
7 1:23 Comparisons
8 1:06 Branching
9 1:51 While Loops
10 1:55 For Loops
11 45:02 3. String Manipulation, Guess and Check, Approximations, Bisection
12 3:05 String Manipulations
13 4:19 For Loops With Strings
14 41:09 4. Decomposition, Abstraction, and Functions
15 2:35 Function Calls
16 3:33 Functions as Arguments
17 41:28 5. Tuples, Lists, Aliasing, Mutability, and Cloning
18 3:29 Tuples
19 2:49 Simple Lists
20 3:06 List Operations
21 1:55 List Aliasing/Mutation
22 48:22 6. Recursion and Dictionaries
23 41:33 7. Testing, Debugging, Exceptions, and Assertions
24 2:04 Black Box and Glass Box Testing
25 1:16 Errors
26 2:41 Exceptions
27 41:44 8. Object Oriented Programming
28 0:50 Class Definition
29 2:21 Class Instance
30 1:26 Methods
31 1:47 Method Call
32 2:12 Special Methods
33 47:28 9. Python Classes and Inheritance
34 1:45 Getters and Setters
35 2:46 Subclass
36 51:26 10. Understanding Program Efficiency, Part 1
37 49:13 11. Understanding Program Efficiency, Part 2
38 48:32 12. Searching and Sorting
source: MIT OpenCourseWare 2017年2月15日
6.0001 Introduction to Computer Science and Programming in Python. Fall 2016
View the complete course: http://ocw.mit.edu/6-0001F16
Instructor: Dr. Ana Bell
6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1 43:06 1. What is Computation?
2 1:20 Shell vs. Editor
3 1:37 Python vs. Math
4 1:40 Bindings
5 43:31 2. Branching and Iteration
6 0:53 Strings
7 1:23 Comparisons
8 1:06 Branching
9 1:51 While Loops
10 1:55 For Loops
11 45:02 3. String Manipulation, Guess and Check, Approximations, Bisection
12 3:05 String Manipulations
13 4:19 For Loops With Strings
14 41:09 4. Decomposition, Abstraction, and Functions
15 2:35 Function Calls
16 3:33 Functions as Arguments
17 41:28 5. Tuples, Lists, Aliasing, Mutability, and Cloning
18 3:29 Tuples
19 2:49 Simple Lists
20 3:06 List Operations
21 1:55 List Aliasing/Mutation
22 48:22 6. Recursion and Dictionaries
23 41:33 7. Testing, Debugging, Exceptions, and Assertions
24 2:04 Black Box and Glass Box Testing
25 1:16 Errors
26 2:41 Exceptions
27 41:44 8. Object Oriented Programming
28 0:50 Class Definition
29 2:21 Class Instance
30 1:26 Methods
31 1:47 Method Call
32 2:12 Special Methods
33 47:28 9. Python Classes and Inheritance
34 1:45 Getters and Setters
35 2:46 Subclass
36 51:26 10. Understanding Program Efficiency, Part 1
37 49:13 11. Understanding Program Efficiency, Part 2
38 48:32 12. Searching and Sorting
Design and Analysis of Algorithms (Spring 2015) by Erik Demaine, Srinivas Devadas, and Nancy Ann Lynch at MIT
# playlist: click the video's upper-left icon
source: MIT OpenCourseWare 2016年12月6日
MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch
6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1 1:23:35 1. Course Overview, Interval Scheduling
2 1:20:35 2. Divide & Conquer: Convex Hull, Median Finding
3 53:46 R1. Matrix Multiplication and the Master Theorem
4 1:20:52 3. Divide & Conquer: FFT
5 30:45 R2. 2-3 Trees and B-Trees
6 1:20:15 4. Divide & Conquer: van Emde Boas Trees
7 1:15:53 5. Amortization: Amortized Analysis
8 1:21:52 6. Randomization: Matrix Multiply, Quicksort
9 39:30 R4. Randomized Select and Randomized Quicksort
10 1:20:56 7. Randomization: Skip Lists
11 1:21:51 8. Randomization: Universal & Perfect Hashing
12 52:03 R5. Dynamic Programming
13 1:24:34 9. Augmentation: Range Trees
14 1:20:08 10. Dynamic Programming: Advanced DP
15 1:21:49 11. Dynamic Programming: All-Pairs Shortest Paths
16 1:22:10 12. Greedy Algorithms: Minimum Spanning Tree
17 22:24 R6. Greedy Algorithms
18 1:22:58 13. Incremental Improvement: Max Flow, Min Cut
19 1:22:33 14. Incremental Improvement: Matching
20 51:12 R7. Network Flow and Matching
21 1:22:27 15. Linear Programming: LP, reductions, Simplex
22 1:25:25 16. Complexity: P, NP, NP-completeness, Reductions
23 45:47 R8. NP-Complete Problems
24 1:21:08 17. Complexity: Approximation Algorithms
25 1:17:43 18. Complexity: Fixed-Parameter Algorithms
26 31:59 R9. Approximation Algorithms: Traveling Salesman Problem
27 1:17:34 19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
28 1:12:03 20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
29 50:19 R10. Distributed Algorithms
30 1:22:01 21. Cryptography: Hash Functions
31 1:24:15 22. Cryptography: Encryption
32 1:20:28 23. Cache-Oblivious Algorithms: Medians & Matrices
33 49:30 R11. Cryptography: More Primitives
34 1:17:41 24. Cache-Oblivious Algorithms: Searching & Sorting
source: MIT OpenCourseWare 2016年12月6日
MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch
6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
1 1:23:35 1. Course Overview, Interval Scheduling
2 1:20:35 2. Divide & Conquer: Convex Hull, Median Finding
3 53:46 R1. Matrix Multiplication and the Master Theorem
4 1:20:52 3. Divide & Conquer: FFT
5 30:45 R2. 2-3 Trees and B-Trees
6 1:20:15 4. Divide & Conquer: van Emde Boas Trees
7 1:15:53 5. Amortization: Amortized Analysis
8 1:21:52 6. Randomization: Matrix Multiply, Quicksort
9 39:30 R4. Randomized Select and Randomized Quicksort
10 1:20:56 7. Randomization: Skip Lists
11 1:21:51 8. Randomization: Universal & Perfect Hashing
12 52:03 R5. Dynamic Programming
13 1:24:34 9. Augmentation: Range Trees
14 1:20:08 10. Dynamic Programming: Advanced DP
15 1:21:49 11. Dynamic Programming: All-Pairs Shortest Paths
16 1:22:10 12. Greedy Algorithms: Minimum Spanning Tree
17 22:24 R6. Greedy Algorithms
18 1:22:58 13. Incremental Improvement: Max Flow, Min Cut
19 1:22:33 14. Incremental Improvement: Matching
20 51:12 R7. Network Flow and Matching
21 1:22:27 15. Linear Programming: LP, reductions, Simplex
22 1:25:25 16. Complexity: P, NP, NP-completeness, Reductions
23 45:47 R8. NP-Complete Problems
24 1:21:08 17. Complexity: Approximation Algorithms
25 1:17:43 18. Complexity: Fixed-Parameter Algorithms
26 31:59 R9. Approximation Algorithms: Traveling Salesman Problem
27 1:17:34 19. Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees
28 1:12:03 20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
29 50:19 R10. Distributed Algorithms
30 1:22:01 21. Cryptography: Hash Functions
31 1:24:15 22. Cryptography: Encryption
32 1:20:28 23. Cache-Oblivious Algorithms: Medians & Matrices
33 49:30 R11. Cryptography: More Primitives
34 1:17:41 24. Cache-Oblivious Algorithms: Searching & Sorting
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