Datasets:

rose-e-wang
/

sight

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train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 3 fields in line 9, saw 4

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 195, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 3 fields in line 9, saw 4
              
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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datasplit string	comment string	video_id string	video_name string	playlist_name string	annotator_H1_general float64	annotator_H1_setup float64	annotator_H1_pedagogy float64	annotator_H1_confusion float64	annotator_H1_gratitude float64	annotator_H1_personal_experience float64	annotator_H1_na float64	annotator_H2_general float64	annotator_H2_setup float64	annotator_H2_pedagogy float64	annotator_H2_confusion float64	annotator_H2_gratitude float64	annotator_H2_personal_experience float64	annotator_H2_na float64	comment_id float64	annotator_openaichat_v1_gratitude float64	annotator_openaichat_v0_setup float64	annotator_openaichat_v8_confusion float64	annotator_openaichat_v47_pedagogy float64	annotator_openaichat_v0_non_english float64	annotator_openaichat_v39_general float64	annotator_openaichat_v45_personal_experience float64	annotator_openaichat_v3_na float64	annotator_openaichat_v46_personal_experience float64	annotator_openaichat_v47_personal_experience float64	annotator_openaichat_v48_personal_experience float64	annotator_openaichat_v49_personal_experience float64	annotator_openaichat_v50_personal_experience float64	annotator_openaichat_v40_general float64	annotator_openaichat_v49_pedagogy float64	annotator_openaichat_v50_pedagogy float64	annotator_openaichat_v51_pedagogy float64	annotator_openaichat_v52_pedagogy float64	annotator_openaichat_v53_pedagogy float64	annotator_openaichat_v54_pedagogy float64	annotator_openaichat_v55_pedagogy float64	annotator_openaichat_v56_pedagogy float64	annotator_openaichat_v57_pedagogy float64	annotator_openaichat_v58_pedagogy float64	annotator_openaichat_v59_pedagogy float64	annotator_openaichat_v60_pedagogy float64	annotator_openaichat_v61_pedagogy float64	annotator_openaichat_v62_pedagogy float64	annotator_openaichat_v63_pedagogy float64	annotator_openaichat_v64_pedagogy float64	annotator_openaichat_v65_pedagogy float64	annotator_openaichat_v66_pedagogy float64	annotator_openaichat_v67_pedagogy float64	annotator_openaichat_v68_pedagogy float64	annotator_openaichat_v69_pedagogy float64	annotator_openaichat_v70_pedagogy float64	annotator_openaichat_v71_pedagogy float64	annotator_openaichat_v72_pedagogy float64	annotator_openaichat_v73_pedagogy float64	68 null	annotator_openaichat_v74_pedagogy float64	annotator_openaichat_v52_personal_experience float64	annotator_openaichat_v73c_pedagogy float64	annotator_openaichat_v73d_pedagogy float64	annotator_openaichat_v74_na float64	annotator_openaichat_v74_clarification float64	annotator_openaichat_v74_non_english float64	annotator_openaichat_v74_gratitude float64	annotator_openaichat_v74_general float64	annotator_openaichat_v74_confusion float64	annotator_openaichat_v74_setup float64	annotator_openaichat_v75_pedagogy float64	annotator_openaichat_v75_clarification float64	annotator_openaichat_v75_personal_experience float64	annotator_openaichat_v75_confusion float64	annotator_openaichat_v75_non_english float64	annotator_openaichat_v75_na float64	annotator_openaichat_v75_setup float64	annotator_openaichat_v75_general float64	annotator_openaichat_v75_gratitude float64
validation	Determinant has been determined. Thank you MIT!	srxexLishgY	18. Properties of Determinants	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	1	0	0	0	0	0	0	1	0	1	54	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1
validation	19:40 if you turn the volume down everything he says changes its meaning	tzoYhe3H5dM	Lec 31: Stokes' theorem \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	0	0	0	1	0	0	0	0	0	0	1	6	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
validation	"Be assured it works just the same way if you have 10,000 variables"	PxCxlsl_YwY	Lec 1: Dot product \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	I suppose that’s why MIT is not for every student and why it is difficult and costly to get into. This course review requires a special level of mastery of the material, which assumes a great deal of understanding and problem practice throughout the course. When a matrix is not square and doesn’t have a unique solution (independent rows or columns) all kinds of scenarios could happen. Adding to that, whether the matrix is orthogonal, orthonormal, symmetric, positive or semi definite, as well as others involving Eigenvectors, values, pivots and determinants. I suppose that the best way to wrap all that information around the head is to start with some simple 2 by 2 or 3 by 3 matrix and experiment with all different scenarios using a good software package to spit out the output and see how the theory works. I am using scientific notebook.	RWvi4Vx4CDc	34. Final Course Review	MIT 18.06 Linear Algebra, Spring 2005	0	0	1	0	0	0	0	1	0	1	0	0	0	0	86	0	0	0	0	0	1	1	0	1	0	0	1	1	1	1	0	1	0	0	0	0	0	1	1	1	0	1	1	1	1	1	1	1	0	0	1	1	1	1	null	1	null	1	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0
validation	21:12 In fact, if we normalize x1, x2, x3 to q1, q2, q3, then A = 0q1q1^T + cq2q2^T + 2q3q3^T (since q's are orthonormal). Any c, which is not necessarily real, can make A symmetric. ---can anyone help me check this statement? I havent found any comment discussing this yet.	HgC1l_6ySkc	32. Quiz 3 Review	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	1	0	0	0	0	0	0	1	0	0	0	8	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0
validation	form is pathetic	J7DzL2_Na80	1. The Geometry of Linear Equations	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	0	0	1	1	0	0	0	0	0	0	176	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
validation	34:43 why "directional second derivative" would not give us a clue of whether it is a min or max? I thought it is a promising way. hmmm.	15HVevXRsBA	Lec 13: Lagrange multipliers \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	1	0	0	0	0	0	0	1	0	0	0	12	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	0	0	0
validation	FCP	hwDRfkPSXng	Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	0	0	0	0	0	1	0	0	0	0	0	0	1	58	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0
validation	Thank you very much! Amazing lectures!	RWvi4Vx4CDc	34. Final Course Review	MIT 18.06 Linear Algebra, Spring 2005	1	0	0	0	1	0	0	1	0	0	0	1	0	0	131	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	1
validation	@seisdoesmatter The chalk's really awesome. Looks thicker than ordinary chalk. It seems a bit like the chalk kids use to paint on the pavement!	srxexLishgY	18. Properties of Determinants	MIT 18.06 Linear Algebra, Spring 2005	0	1	0	0	0	0	0	0	1	0	0	0	0	0	27	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
validation	His teaching style seems casual and intuitive. I go to a small public college and the course is much more formal and proof driven. These lectures are a great addition to (as well as a nice break from) formal proofs. Thanks MIT!	yjBerM5jWsc	9. Independence, Basis, and Dimension	MIT 18.06 Linear Algebra, Spring 2005	0	0	1	0	1	1	0	1	0	1	0	0	1	0	70	1	0	0	1	0	1	1	0	1	1	1	1	1	1	1	0	1	1	1	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0	1	null	1	0	1	1	0	0	0	1	1	0	0	1	0	1	0	0	0	0	1	1
validation	really useful, thanks!!	15HVevXRsBA	Lec 13: Lagrange multipliers \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	1	0	0	0	1	0	0	1	0	0	0	1	0	0	191	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	1
validation	Prerequisites please??	j9WZyLZCBzs	1. Probability Models and Axioms	6.041 Probabilistic Systems Analysis and Applied Probability	0	0	0	1	0	0	0	0	0	0	1	0	0	0	108	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	Marvellous!!!	k3AiUhwHQ28	25. Stochastic Gradient Descent	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	1	0	0	0	0	0	0	1	0	0	0	0	0	0	103	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	feels like too many things a squished into one lecture	EObHWIEKGjA	7. Discrete Random Variables III	6.041 Probabilistic Systems Analysis and Applied Probability	1	0	0	0	0	0	0	1	0	0	0	0	0	0	173	0	0	0	0	0	1	0	0	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	null	0	null	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	Keep up the good work🙏	LY7YmuDbuW0	Lecture 1: Sets, Set Operations and Mathematical Induction	MIT 18.100A Real Analysis, Fall 2020	1	0	0	0	0	0	0	1	0	0	0	0	0	0	99	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	for 2 by 2 systems the normal method at school is fine but it's too long and it's much quicker to write it in a matrix and the pivots and words like that are just terms to describe what he is doing	QVKj3LADCnA	2. Elimination with Matrices.	MIT 18.06 Linear Algebra, Spring 2005	0	0	1	0	0	0	0	0	0	0	0	0	1	0	174	0	0	0	0	0	1	1	0	1	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	No.	MsIvs_6vC38	4. Factorization into A = LU	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	0	0	1	0	0	0	0	0	0	1	104	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	so substitute infinity minus one and chart the resultant here and now on the time line relax allow the unknown as infinite as well same graph pulse the fibronchi generator chart for rms the rms positive on sine only so return to chart and now condense the univariable into quantum zero	QVKj3LADCnA	2. Elimination with Matrices.	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	0	0	1	0	0	0	0	0	0	1	192	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0
validation	can anyone explain how the 5th column of w8 came out to be?	Xa2jPbURTjQ	3. Orthonormal Columns in Q Give Q'Q = I	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	0	0	1	0	0	0	0	0	0	1	0	0	0	169	0	0	1	0	0	0	1	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	1	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
validation	Simply excellent!! The Internet can save the world	YP_B0AapU0c	Lec 16: Double integrals \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	1	0	0	0	0	0	1	1	0	0	0	0	0	0	119	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	Prof. Strang. Please dont wink at me. I'm too shy. : P Kidding! I've grown used to it from your linear algebra series 😅 Thanks for your work and all the enthusiasm.	Cx5Z-OslNWE	Course Introduction of 18.065 by Professor Strang	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	0	0	0	1	0	1	1	0	0	0	1	0	0	109	1	0	0	0	0	1	0	1	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	1	1	0	0	0	0	0	0	0	1	0	1	1
validation	are the online assignments available for this course?	YiqIkSHSmyc	Lecture 1: The Column Space of A Contains All Vectors Ax	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	0	0	1	0	0	0	0	0	0	1	0	0	0	166	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	Too funny, people need to see this. acesofclubs 1 year ago Mafia Mob moment at 11:36	5q_3FDOkVRQ	Lec 5 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	0	0	0	0	0	0	1	151	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
validation	this lecturer is the best lecturer i've ever had. never encounterd such clear explanations! very recommende :-)	j9WZyLZCBzs	1. Probability Models and Axioms	6.041 Probabilistic Systems Analysis and Applied Probability	1	0	1	0	0	0	0	1	0	0	0	0	0	0	199	0	0	0	0	0	1	0	0	0	1	1	0	0	1	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	This class is essentially week one of a Machine Learning class XD	tBUHRpFZy0s	24. Classical Inference II	6.041 Probabilistic Systems Analysis and Applied Probability	1	0	0	0	0	0	1	1	0	0	0	0	0	0	142	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0
validation	Why the hell does my video freezes when it reaches 00:27 secods it feels like my ethernet service provider is preventing me from lisnening to a great lec! anyways need some coffee...	7K1sB05pE0A	Lec 1 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	1	0	0	0	0	0	1	158	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	the audio is so low even when i turn up all volume. prof needs to put his mike anywhere but his chest plz. specifically talking about parts around 33:18. mans whispering. and you can't even hear what the students are asking. but i guess since this is opencourseware, i can't complain much about free college lectures.	rLlZpnT02ZU	4. Parametric Inference (cont.) and Maximum Likelihood Estimation	MIT 18.650 Statistics for Applications, Fall 2016	0	1	0	0	0	0	0	0	1	0	0	0	0	0	196	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	null	0	null	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0
validation	Around 34:00 : what about the case there AC - B^2 > 0 but A = 0? I take it that is also a case where we have a local max, since -B^2 is always negative; i'm just sorta surprised no-one noticed the omission?	3_goGnJm5sA	Lec 10: Second derivative test; boundaries & infinity \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	1	0	0	0	0	0	0	1	0	0	0	40	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	null	0	null	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0
validation	Thank you so much for putting analysis online!	LY7YmuDbuW0	Lecture 1: Sets, Set Operations and Mathematical Induction	MIT 18.100A Real Analysis, Fall 2020	0	0	0	0	1	0	0	0	0	0	0	1	0	0	127	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
validation	is this course applicable for lab analysis of data using statistical methods ? im a physics student wondering if this is worth the time to learn	VPZD_aij8H0	1. Introduction to Statistics	MIT 18.650 Statistics for Applications, Fall 2016	0	0	0	1	0	1	0	0	0	0	1	0	0	0	186	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	Awesome Teacher Gibert Strang	FX4C-JpTFgY	3. Multiplication and Inverse Matrices	MIT 18.06 Linear Algebra, Spring 2005	1	0	0	0	0	0	0	1	0	0	0	0	0	0	42	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	Is a hyperbola in high dimensional spaces called a hyperhyperbola?	vF7eyJ2g3kU	27. Positive Definite Matrices and Minima	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	1	0	0	0	0	0	0	1	0	0	1	90	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
validation	Professore: "How many of you actually knew about vectors before that?" Class: -______________- ... seriously?	PxCxlsl_YwY	Lec 1: Dot product \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	0	0	0	1	0	0	0	0	0	0	1	112	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	This was great. I only need some Harvard friends I can impress.	P7a4bjE6Crk	12. Iterated Expectations	6.041 Probabilistic Systems Analysis and Applied Probability	1	0	0	0	0	0	1	1	0	0	0	0	0	1	150	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
validation	41:21 it would help if it were in shot	NcPUI7aPFhA	Lecture 8: Norms of Vectors and Matrices	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	1	0	0	0	0	0	0	1	0	0	0	0	0	14	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	null	0	null	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
validation	The only reason I can see that you'd be opposed to this is perhaps if you think that American workers and businesses cannot keep up? Or just threatened and xenophobic of non-Americans? Well, if rising standards serves as a motivation, then American businesses have to step up their games and become better. Which is better for you. It's not all about direct national interest. And as an international student who is not going to MIT, I greatly appreciate this gesture (which doesn't cost them much).	Pd2xP5zDsRw	Lec 20 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	0	0	0	0	0	0	1	141	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	Thank you, professor Strang and all guys insist on this perfect linear algebra lecture. I started this lecture on 23/12/2021 and completed it on 08/01/2022 in Hong Kong, just before my new semester. I will remember this 18.06 forever, and professor Strang will be the best linear algebra teacher in my heart!	RWvi4Vx4CDc	34. Final Course Review	MIT 18.06 Linear Algebra, Spring 2005	1	0	0	0	1	1	0	1	0	0	0	1	1	0	133	1	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	1
validation	why do we need to make the eigen vector as small as possible ?	wrEcHhoJxjM	23. Accelerating Gradient Descent (Use Momentum)	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	0	0	0	1	0	0	0	0	0	0	1	0	0	0	206	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
validation	Thanks for MIT OCW and Prof.Auroux.	24v9onS9Kcg	Lec 35: Final review (cont.) \| MIT 18.02 Multivariable Calculus, Fall 2007	MIT 18.02 Multivariable Calculus, Fall 2007	0	0	0	0	1	0	0	0	0	0	0	1	0	0	137	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
validation	With all my endurance I listen to this saga until this lecture just to understand Linear Regression	Y_Ac6KiQ1t0	15. Projections onto Subspaces	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	0	1	1	1	0	0	0	0	0	0	161	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	null	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	i dont think this is what youtube was made for ._.	7K1sB05pE0A	Lec 1 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	0	0	0	0	0	0	1	181	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	By the chain rule, (d/dx) f(g) when g is a function of x is g' * f '(g). In this case, the "g" is (kx) so you can get the answer by using the chain rule.	9v25gg2qJYE	Lec 6 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	0	0	0	0	0	0	1	47	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	0	null	0	null	1	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
validation	I admire this person a lot, it shows his passion and dedication. At his advanced age, I hope I have the same love at work as he does.	7UJ4CFRGd-U	An Interview with Gilbert Strang on Teaching Linear Algebra	MIT 18.06 Linear Algebra, Spring 2005	1	0	0	0	0	0	0	1	0	0	0	0	0	0	74	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
validation	Engineering students must love this	BSAA0akmPEU	Lec 9 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	1	0	0	0	0	0	0	55	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
validation	This man just truly loves linear algebra, and it's fantastic.	or6C4yBk_SY	Lecture 2: Multiplying and Factoring Matrices	MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018	1	0	0	0	0	0	0	1	0	0	0	0	0	0	148	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
validation	He's such a great instructor	QVKj3LADCnA	2. Elimination with Matrices.	MIT 18.06 Linear Algebra, Spring 2005	1	0	0	0	0	0	0	1	0	0	0	0	0	0	69	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0
validation	Thank you so much!!	RWvi4Vx4CDc	34. Final Course Review	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	1	0	0	0	0	0	0	1	0	0	130	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
validation	Why not Leipzig continuous and just bounded?	78vN4sO7FVU	Lecture 2: Bounded Linear Operators	MIT 18.102 Introduction to Functional Analysis, Spring 2021	0	0	0	1	0	0	0	0	0	0	1	0	0	0	157	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
validation	Thanks Professor Gilbert Strang	7UJ4CFRGd-U	An Interview with Gilbert Strang on Teaching Linear Algebra	MIT 18.06 Linear Algebra, Spring 2005	0	0	0	0	1	0	0	0	0	0	0	1	0	0	134	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	null	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
validation	He's just showing applications of linear algebra, not teaching them. That's why it seems "sloppy". You just can't teach Fourier Transform in 30 mins.	M0Sa8fLOajA	26. Complex Matrices; Fast Fourier Transform	MIT 18.06 Linear Algebra, Spring 2005	0	0	1	0	0	0	0	1	0	1	0	0	0	0	68	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	1	null	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	0	0	1	0	null	1	null	0	null	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
validation	Wtf, i learnt this when I was 15. How is MIT one of the best uni in the world??	7K1sB05pE0A	Lec 1 \| MIT 18.01 Single Variable Calculus, Fall 2007	MIT 18.01 Single Variable Calculus, Fall 2006	0	0	0	0	0	0	1	0	0	0	0	0	1	0	163	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	null	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0

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