AI class errata

These are my notes on errata I've discovered in the AI class coursework. As I discover errata I add it to the bottom of the table.

Videos

Video	Time	Is	Should be
4G5mH4FW-WY	0:51	curvatic	quadratic
0RmqLOxexh4	0:26	corresponding	closed-form
0RmqLOxexh4	0:41	interation	iteration
0RmqLOxexh4	0:54	up with it	update it
rAcwpZJqAZA	0:46	local minimum	local minima
dKKigX6nhyU	0:05	quadratic arrow	quadratic error
R1o9wbhnv94	0:43	plausible	closed-form
yOSGC67bOIk	0:50	sets	data sets
yOSGC67bOIk	1:53	grade descent	gradient descent
yOSGC67bOIk	1:57	grade descent	gradient descent
yOSGC67bOIk	3:10	and the error is zero, then no update occurs	then the error is zero, and no update occurs
xRf9wAeU1kI	1:43	robust-ness	robustness
xRf9wAeU1kI	2:00	integer	iterative
xRf9wAeU1kI	3:32	so that just	plotted just
xRf9wAeU1kI	3:49	Map	Mapped
xRf9wAeU1kI	4:36	to write	to derive
xRf9wAeU1kI	4:44	These messages	These methods
ZLEilYyt28c	0:35	condition probabilities	conditional probabilities
PoRpuj4bijU	0:00	is an easy answer	is easily answered
tOSoqfK9UNE	1:05	If your graph input	If you graph your input
tOSoqfK9UNE	1:35	they are to be one	there ought to be one
tOSoqfK9UNE	1:36	your're	you're
kFwsW2VtWWA	0:04	are seen not to be completely random determinants	seem not to be completely randomly determinate
EZEOXNFgu8M	0:08	interpretively assume	typically assume
EZEOXNFgu8M	0:38	structure and data	structure in data
EZEOXNFgu8M	1:21	drawing signal	joint signal
W2dkDmHFMWg	2:04	They were derived	They will be derived
zaKjh2N8jN4	0:18	found interatively	found iteratively
zaKjh2N8jN4	0:34	Euclidian	Euclidean
zaKjh2N8jN4	1:28	has attained the center	is attained at the center
myqnyxkdQpc	0:17	corresponding step	correspondence step
myqnyxkdQpc	1:10	local minimum	local minima
3zlXl82LUVI	0:02	one interation	one iteration
_DhelJs0BFc	0:44	horizontal access	horizontal axis
_DhelJs0BFc	2:04	then it is	than it is
_DhelJs0BFc	2:08	periphery summary over here	periphery somewhere over here
rMcw3uu4efY	1:55	complete the derivative for spectrum mu	compute the derivative with respect to mu
rMcw3uu4efY	2:03	we can still get this	we instead get this
rMcw3uu4efY	2:11	next to zero	it's still zero
rMcw3uu4efY	2:56	stresses internal	is just its internal
pRGEQy7BgiY	0:04	multivariant	multivariate
mlz-1yfyeoU	0:06	the fit from data	how to fit them from data
1CWDWmF0i2s	0:47	Their movement is smooth away	They move in a smoother way
tTr7547zVCc	0:07	sum of all possible	sum over all possible
tTr7547zVCc	0:45	should we call	which we will call
TFViJ3P6NwM	0:12	specifically M1 sigma	specifically mu and sigma
DODedtJZ3FA	0:17	first situation	first iteration
_-Ol1cXIWvQ	0:40	memorisation of a criterion	minimization of a criterion
_-Ol1cXIWvQ	1:40	tests would show	tests should show
lDyEk72TezE	0:04	learning avenues	learning algorithms
lDyEk72TezE	0:26	we're going to	we want to
AaSibhWmkQM	0:06	almost information	almost no information
5m6TeKw_e1M	0:12	to perfect the data	to project the data
5m6TeKw_e1M	1:17	Your axes are	Your x's are
5m6TeKw_e1M	1:23	4 x1	For x1
5m6TeKw_e1M	2:31	Where this	Whereas this
5m6TeKw_e1M	2:36	So this single	So this is the single
VxAMBkDUfeg	0:31	we would do	would do
VxAMBkDUfeg	0:39	"K" mean	k-means
P-LEH-AFovE	0:21	annotation of	a notion of
P-LEH-AFovE	1:27	realtive	relative
P-LEH-AFovE	2:39	tivectors	vectors
P-LEH-AFovE	2:52	an item of value	an eigenvalue
P-LEH-AFovE	3:56	It strikes the	Extracts the
P-LEH-AFovE	4:05	clustering the	cluster in the
P-LEH-AFovE	4:18	they're affinity	their affinity
pszEzBql4bw	0:17	represent in reason	represent and reason
pszEzBql4bw	0:55	agents model	agent's model
_VjyktjNMoM	0:24	of\	of
_VjyktjNMoM	0:24	unlike improbability	unlike in probability
Th_wM93aF94	2:38	there exists in x	there exists an x
JcQrAin3_V8	0:10	O is true	always true
lb1bEUa9WXg	1:21	location its in	location it's in
lb1bEUa9WXg	1:59	of the least states	of belief states
Bxd-j9s82Z8	0:04	you're	your
SzeJX57N-_I	2:33	this to predict and update	this predict and update
cfYnEgrVemA	0:11	start to clean	suck to clean
cfYnEgrVemA	0:13	actually turn; and you	actually turn, and you
-o9E15BAL3o	0:35	they'll be	there'll be
DgH6NaJHfVQ	0:06	together from the material	together some of the material
DgH6NaJHfVQ	0:14	classes of under uncertainty	classes on uncertainty
9D35JSWSJAg	0:33	partial observer	partially observable
9D35JSWSJAg	0:40	of his aspects	of these aspects
9D35JSWSJAg	0:51	was the casting	with stochastic
9D35JSWSJAg	0:55	about photos as partial observable	about fully observable versus partially observable
9D35JSWSJAg	1:06	all of our evidence falls	all of our algorithms fall
9D35JSWSJAg	1:09	right first	breadth first
9D35JSWSJAg	2:03	process by a graph	process is by a graph
9D35JSWSJAg	2:35	state's transition matrix	state transition matrix
9D35JSWSJAg	2:47	state is prime	state S'
9QMZQkKuYjo	0:18	of the this robot	of this robot