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Results of Search: 176,291 lines — Showing 19421 - 19440.
| Course | Level | Date | Clip# | Line | Interlocutor | Content |
|---|---|---|---|---|---|---|
| Oral Assessments | Graduate | 2019-08-14 | 1 | 122 | T01 | your students↘ |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 123 | UNK | (1.8) |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 124 | S01 | mm: i think (0.9) a very: (.) a very important way is |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 125 | S01 | to: (.) uh illustrate some examples↘ |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 126 | S01 | u:m (0.6) for ex- um: (1.8) if you only take- uh if you only |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 127 | S01 | talk about some very abstract uh (.) concepts ⌈uh |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 128 | T01 | ⌊mm hm |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 129 | S01 | people often ☺hate about it☺ and uh ⌈yeah but if you |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 130 | T02 | ⌊&hm |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 131 | S01 | use some uh actual: uh actual examples↘ for example→ uh |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 132 | S01 | some (0.8) uh (3.3) eh for example if you talk about the |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 133 | S01 | ∆adversarial machine learning∆→ you can (0.4) give an example |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 134 | S01 | of the classifier between cats and (.) cats and birds→ a⌈nd |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 135 | T01 | ⌊mm hm |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 136 | S01 | that ma- will make things clearer↘ |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 137 | T01 | right↘ (0.5) ↑good↘ |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 138 | UNK | (0.7) |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 139 | T01 | o↑kay thank you→ |
| Oral Assessments | Graduate | 2019-08-14 | 1 | 140 | UNK | @End |
| Oral Assessments | Graduate | 2019-08-14 | 2 | 2 | UNK | @UTF8 |