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Oral Assessments Graduate 2019-08-14 3 56 T01 alright↘ so→ (.) you have one minute to prepare
Oral Assessments Graduate 2019-08-14 3 57 UNK (1.9)
Oral Assessments Graduate 2019-08-14 3 58 S01 can i use these↗
Oral Assessments Graduate 2019-08-14 3 59 T01 yep
Oral Assessments Graduate 2019-08-14 3 60 UNK (61.3)
Oral Assessments Graduate 2019-08-14 3 61 T01 °okay you can begin°↘
Oral Assessments Graduate 2019-08-14 3 62 S01 okay
Oral Assessments Graduate 2019-08-14 3 63 S01 ☉so☉ um (.) today i will talk about a term of
Oral Assessments Graduate 2019-08-14 3 64 S01 ∆adversarial machine learning∆→ and uh it's
Oral Assessments Graduate 2019-08-14 3 65 S01 a term in the field of (.) security of
Oral Assessments Graduate 2019-08-14 3 66 S01 machine learning↘
Oral Assessments Graduate 2019-08-14 3 67 S01 and before we illustrate the definition→ uh i- i
Oral Assessments Graduate 2019-08-14 3 68 S01 will talk about→ (.) the: definition of (0.4) uh
Oral Assessments Graduate 2019-08-14 3 69 S01 (0.8) machine learning ↑models first↘
Oral Assessments Graduate 2019-08-14 3 70 S01 the machine learning models they are usually
Oral Assessments Graduate 2019-08-14 3 71 S01 some (.) classifiers such as to classify the
Oral Assessments Graduate 2019-08-14 3 72 S01 images of (.) dogs and cats into their original
Oral Assessments Graduate 2019-08-14 3 73 S01 the true cla:sses↘
Oral Assessments Graduate 2019-08-14 3 74 UNK (0.4)
Oral Assessments Graduate 2019-08-14 3 75 S01 uh (0.8) so uh and what machine learning uh