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Transcript Table

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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-16 4 56 S01 so there are so many method (0.5) hm in numerical
Oral Assessments Graduate 2019-08-16 4 57 S01 optimization↗
Oral Assessments Graduate 2019-08-16 4 58 UNK (.)
Oral Assessments Graduate 2019-08-16 4 59 S01 and I I I will not (.) go into the detail but (.) I will
Oral Assessments Graduate 2019-08-16 4 60 S01 (0.4) first give you some (0.6) terms↘
Oral Assessments Graduate 2019-08-16 4 61 UNK (0.9)
Oral Assessments Graduate 2019-08-16 4 62 S01 one is a t- newton→ (0.4) hm::: gradient↘
Oral Assessments Graduate 2019-08-16 4 63 UNK (4.7)
Oral Assessments Graduate 2019-08-16 4 64 S01 ⁇°is°⁇
Oral Assessments Graduate 2019-08-16 4 65 UNK (4.7)
Oral Assessments Graduate 2019-08-16 4 66 S01 ⁎and⁎
Oral Assessments Graduate 2019-08-16 4 67 UNK (0.8)
Oral Assessments Graduate 2019-08-16 4 68 S01 xxx
Oral Assessments Graduate 2019-08-16 4 69 UNK (5.5)
Oral Assessments Graduate 2019-08-16 4 70 S01 so (.) in (.) this (.) lecture↗ I will not ⁇be able to tell⁇
Oral Assessments Graduate 2019-08-16 4 71 S01 that↘ but I will (0.3) like talk about the advantages
Oral Assessments Graduate 2019-08-16 4 72 S01 and disadvanatges of this xxx ⁇method⁇↘
Oral Assessments Graduate 2019-08-16 4 73 UNK (0.7)
Oral Assessments Graduate 2019-08-16 4 74 S01 so (2.3) as (0.5) we m- (0.3) we mention for the (0.4) xxx
Oral Assessments Graduate 2019-08-16 4 75 S01 numerical optimization method to uh like xxx the (0.9)
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