2018-03-16

Digital Photography (Spring 2016) by Marc Levoy at Stanford U


source: Marc Levoy 2016年8月27日
These video lectures on digital photography are from my Stanford course CS 178 that was recorded at Google in Spring 2016. Links to all 18 videos, my slides (in PDF form), and the course applets and assignments are on the Schedule page of the course web site: http://sites.google.com/site/marclevo...
Playlist of all 18 lectures: https://www.youtube.com/playlist?list...
Note that the quality of the audio is poor in this lecture (#1) due to an inferior microphone; subsequent lectures have better audio.
Regarding captioning and translation, all of the lectures have auto-captioning enabled. This also enables auto-translation if you switch the captioning language. The quality of this captioning is uneven, and is probably poor in lecture #1 due to its inferior audio quality. People are welcome to help me by captioning these lectures - in English or whatever language they speak!
To help caption all of these lectures, follow this link: https://www.youtube.com/timedtext_cs_...
Finally, I apologize for being unable to field the many good technical questions these lectures are generating, including many that I am receiving through other channels. Given my day job at Google, I simply cannot afford the time. I am sorry!

Lecture 1 (21mar16).mp4 1:15:21
Lecture 2 (23mar16).mp4 1:13:18
Lecture 3 (28mar16).mp4 1:16:19
Lecture 4 (30mar16).mp4 1:05:22
Lecture 5 (04apr16).mp4 1:03:57
Lecture 6 (06apr16).mp4 1:16:22
Lecture 7 (11apr16).mp4 1:15:32
Lecture 8 (13apr16).mp4 1:14:24
Lecture 9 (18apr16).mp4 1:21:22
Lecture 10 (20apr16).mp4 1:14:33
Lecture 11 (25apr16).mp4 1:14:56
Lecture 12 (02May16).mp4 1:14:45
Lecture 13 (04May16).mp4 1:15:01
Lecture 14 (09May16).mp4 1:06:21
Lecture 15 (16may16).mp4 1:06:41
Lecture 16 (18may16).mp4 1:11:32
Lecture 17 (23May16).mp4 1:16:23
Lecture 18 (01jun16).mp4 1:00:59

Probabilistic Analysis (Fall 2017) by Çağın Ararat at Bilkent University

# playlist: click the video's upper-left icon

source: BilkentUniversitesi       2017年9月22日
IE 523 Probabilistic Analysis
Department of Industrial Engineering
Axiomatic construction of probability theory, properties of probability, conditional probability, independence. Discrete and continuous random variables and vectors (distribution function, expectation, variance, moments). Chebyshev inequality and law of large numbers. Conditional expectation. Transformations of random variables. Generating and characteristics functions. Asymptotic methods in probability theory, types of convergence of random variables. Sums of independence random variables, central limit theorem, Poisson theorem. Selected topics.

Lecture 01: Introduction, Random Experiment
Lecture 02: Sample Space, Event
Lecture 03: Sigma Algebra, Generated Sigma Algebra
Lecture 04: Borel Sigma Algebra, Trace
Lecture 05: Monotone Class Theorem
Lecture 06: Probability Measure
Lecture 07: Probability Measure, Lebesgue Measure
Lecture 08: Measure Space
Lecture 09: Random Variables, Measurable Functions
Lecture 10: Operations with Random Variables
Lecture 11: Limits of Random Variables
Lecture 12: Monotone Class Theorem for Functions
Lecture 13: Expectations and Lebesgue Integrals
Lecture 14: Monotone Convergence Theorem
Lecture 15: Limit Theorems for Integrals
Lecture 16: Examples of Integrals, Insensitivity
Lecture 17: Expectation, Distribution
Lecture 18: Radon - Nikodym Theorem
Lecture 19: Discrete Random Variables
Lecture 20: Continuous Random Variables
Lecture 21: Laplace Transform
Lecture 22: Fourier Transform
Lecture 23: Existence of Random Variables
Lecture 24: Product Spaces
Lecture 25: Measurability in Product Spaces
Lecture 26: Measures on Product Spaces
Lecture 27: Measures on Product Spaces
Lecture 28: Product of a Kernel and a Measure
Lecture 29: Fubini and Tonelli Theorems
Lecture 30: Joint Distributions
Lecture 31: Independence
Lecture 32: Joint Distributions and Independence
Lecture 33: Gaussian Random Vectors
Lecture 34: Conditional Expectation
Lecture 35: Conditional Determinism, Tower Property
Lecture 36: Conditional Expectation given A Random Variable
Lecture 37: Regular Conditional Probability
Lecture 38: Conditional Distributions
Lecture 39: Conditioning Examples
Lecture 40: Introduction to Stochastic Processes
Lecture 41: Ionescu - Tulcea Theorem
Lecture 42: Bernoulli Processes
Lecture 43: Almost Sure Convergence
Lecture 44: Convergence in Probability
Lecture 45: Lp Spaces
Lecture 46: Convergence in Lp
Lecture 47: Law of Large Numbers, Central Limit Theorem

Calculus I (Fall 2017) by Ali Sinan Sertöz at Bilkent University

# playlist: click the video's upper-left icon

source: BilkentUniversitesi      2017年9月22日
Calculus I - MATH 101
Limits and continuity. Differentiation and applications (linearization, optimization, curve sketching, l'Hôpital's rule). Integration and applications (areas, volumes, arc lengths, surface areas). Transcendental functions. Integration techniques. Improper integrals.

Lecture 01: Limits I
Lecture 02: Limits II
Lecture 03: One Sided Limits
Lecture 04: Problem on Limits
Lecture 05: Sandwich Theorem
Lecture 06: Continuous Functions
Lecture 07: Intermediate Value Theorem
Lecture 08: Problems
Lecture 09: Derivatives
Lecture 10: Rules of Derivatives
Lecture 11: Problems on Derivatives
Lecture 12: Derivatives of Trigonometric Functions
Lecture 13: Derivatives of Trigonometric Functions
Lecture 14: Chain Rule & Implicit Differentiation
Lecture 15: Problems
Lecture 16: Problems
Lecture 17: Related Rates
Lecture 18: Related Rates
Lecture 19: Linearization
Lecture 20: Minimum & Maximum
Lecture 21: Fermat's Theorem
Lecture 22: Mean Value Theorem
Lecture 23: Problems
Lecture 24: Shapes of a Graph
Lecture 25: Curve Sketching I: Horizontal Asymtotes
Lecture 26: Curve Sketching II: Slant Asymptotes
Lecture 27: Optimization Problems I
Lecture 28: Optimization Problems II
Lecture 29: Optimization Problems III
Lecture 30: Optimization Problems IV
Lecture 31: Newton's Method
Lecture 32: Antiderivatives
Lecture 34: Definite Integrals
Lecture 35: The Fundamental Theorem of Calculus
Lecture 36: Indefinite Integrals
Lecture 37: The Substitution Rule
Lecture 38: Separable Equations
Lecture 39: Areas between Curves
Lecture 40: Areas between Curves
Lecture 41: Volumes by Cylindrical Shells
Lecture 42: The Natural Logarithm Function
Lecture 43: Log & Exponential Functions
Lecture 44: The Exponential Functions
Lecture 45: General Logarithm and Exponentials
Lecture 46: Inverse Trigonometric Functions I
Lecture 47: Inverse Trigonometric Functions II
Lecture 48: L'Hospital's Rule I
Lecture 49: L'Hospital's Rule II
Lecture 50: Integration by Parts
Lecture 51: Trigonometric Integrals
Lecture 52: Trigonometric Substitution
Lecture 53: Partial Fractions Method
Lecture 54: Improper Integrals I
Lecture 55: Improper Integrals II
Lecture 56: Problems

Developmental Psychology (Spring 2017) by Hande Ilgaz at Bilkent University

# playlist: click the video's upper-left icon

source: BilkentUniversitesi      2017年11月5日
Developmental Psychology  (PSYC 240)

Lecture 01: Chapter 1 - History, Theory and Applied Directions
Lecture 02: Chapter 1 - History, Theory and Applied Directions (cont'd)
Lecture 03: Chapter 2 - Research Strategies, Ethical Concerns
Lecture 04: Chapter 2 - Research Strategies, Ethical Concerns (cont'd)
Lecture 05: Chapter 3 - Biological Foundations, Prenatal Development, and Birth
Lecture 06: Chapter 3 - Biological Foundations, Prenatal Development, and Birth (cont'd)
Lecture 07: Chapter 4 - Infancy: Early Learning, Motor Skills, and Perceptual Capacities
Lecture 08: Chapter 4 - Infancy: Early Learning, Motor Skills, and Perceptual Capacities (cont'd)
Lecture 09: Chapter 4 - Infancy: Early Learning, Motor Skills, and Perceptual Capacities (cont'd)
Lecture 10: Chapter 4 - Infancy: Early Learning, Motor Skills, and Perceptual Capacities (cont'd)
Lecture 11: Chapter 4 - Infancy: Early Learning, Motor Skills, and Perceptual Capacities (cont'd); Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives
Lecture 12: Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives (cont'd)
Lecture 13: Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives (cont'd)
Lecture 14: Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives (cont'd)
Lecture 15: Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives (cont'd)
Lecture 16: Chapter 6 - Cognitive Development: Piagetian, Core Knowledge, and Vygotskian Perspectives (cont'd); Chapter 7 - Cognitive Development: An Information - Processing Perspective
Lecture 17: Chapter 7 - Cognitive Development: An Information - Processing Perspective
Lecture 18: Chapter 7 - Cognitive Development: An Information - Processing Perspective
Lecture 19: Chapter 7 - Cognitive Development: An Information - Processing Perspective; Chapter 9 - Language Development
Lecture 20: Chapter 9 - Language Development
Lecture 21: Chapter 9 - Language Development
Lecture 22: Chapter 10 - Emotional Development
Lecture 23: Chapter 10 - Emotional Development (cont'd)
Lecture 24: Chapter 10 - Emotional Development (cont'd)
Lecture 25: Chapter 10 - Emotional Development (cont'd); Chapter 11 - Self and Social Understanding
Lecture 26: Chapter 11 - Self and Social Understanding
Lecture 27: Chapter 12 - Moral Development
Lecture 28: Chapter 12 - Moral Development (cont'd)
Lecture 29: Chapter 12 - Moral Development (cont'd)
Lecture 30: Chapter 13 - Development of Sex Differences and Gender Roles
Lecture 31: Chapter 13 - Development of Sex Differences and Gender Roles
Lecture 32: Chapter 13 - Development of Sex Differences and Gender Roles
Lecture 33: Chapter 15 - Peers, Media and Schooling
Lecture 34: Chapter 15 - Peers, Media and Schooling (cont'd)

Game Theory I (Spring 2018) by Tarık Kara at Bilkent University


source: BilkentUniversitesi    2018年2月9日
ECON 439 Game Theory I
Department of Economics
This course is an introduction to the theory of games. Games theory provides a set of analytical tools that can be used to model the interactions of decision-makers (consumers, firms, politicians, government, etc). The course introduces the basic theory of noncooperative game theory. A variety of applications will be discussed.

Lecture 01: Introduction
Lecture 02: Introduction to Strategic Form Games
Lecture 03: Modeling Games in Strategic Form
Lecture 04: Modeling Games in Strategic Form (cont'd)
Lecture 05: Strictly Dominant & Dominant Strategy Solution Concept
Lecture 06: Pareto Efficiency
Lecture 07: Best Response Correspondence & (strictly) Dominant Strategies
Lecture 08: Best Response Correspondence & (strictly) Dominant Strategies (cont'd)
Lecture 09: Best Response Correspondence
Lecture 10: Iterated Elimination of Dominated Strategies & Cournot's Oligopoly
Lecture 10: Iterated Elimination of Strictly Dominated Strategies in Cournot's Duopoly

(theme videos) Government: Declassified (from TED-Ed)


source: TED-Ed

(theme videos) The World's People and Places (from TED-Ed)


source: TED-Ed

(theme videos) The Way We Think (from TED-Ed)


source: TED-Ed

(theme videos) Making the Invisible Visible (from TED-Ed)


source: TED-Ed

(theme videos) Math in Real Life (from TED-Ed)


source: TED-Ed