imported>Mmustafaeva: Добавление информации о курсе

2026-04-19T21:04:38Z

Добавление информации о курсе

Новая страница

==О курсе==

Занятия проводятся в Zoom по вторникам в 19:40.

==Контакты==

Преподаватель: Петр Гринберг

==Материалы курса==
Ссылка на плейлист курса на YouTube: [https://www.youtube.com/playlist?list=PLmA-1xX7IuzAM3T8NxmmnEjT72rim0HYJ YouTube Playlist]
{| class="wikitable"
! Номер занятия
! Лекция / Разбираемая статья
|-
| 1
| Создание презентаций и рассказ докладов
|-
| rowspan="3" | 2
| ToolGen: Unified Tool Retrieval and Calling via Generation
|-
| Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
|-
| Visual Instruction Tuning
|-
| rowspan="3" | 3
| Scalable Diffusion Models with Transformers
|-
| Direct Preference Optimization: Your Language Model is Secretly a Reward Model
|-
| Graph Attention Networks
|-
| rowspan="3" | 4
| Voyager: An Open-Ended Embodied Agent with Large Language Models
|-
| VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks
|-
| ReAct: Synergizing Reasoning and Acting in Language Models
|-
| rowspan="3" | 5
| On Efficient Distillation from LLMs to SLMs
|-
| Efficiently Modeling Long Sequences with Structured State Spaces
|-
| Efficient Memory Management for Large Language Model Serving with PagedAttention
|-
| rowspan="3" | 6
| Large Language Diffusion Models
|-
| Toolformer: Language Models Can Teach Themselves to Use Tools
|-
| Fast Inference from Transformers via Speculative Decoding
|-
| 7
| Инструкция по написанию научных текстов
|-
| rowspan="3" | 8
| ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
|-
| The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
|-
| Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
|-
| rowspan="2" | 9
| Предзащита ВКР
|-
| Предзащита ВКР
|-
| rowspan="3" | 10
| Предзащита ВКР
|-
| Предзащита ВКР
|-
| Предзащита ВКР
|}

==Формула оценивания==

Накопленная = 0.4 * О_Квиз + 0.4 * О_Видео + 0.1 * О_Ревью + 0.1 * О_Мета-Ревью

Оценка = 10 если Накопленная >= 5, иначе 0.

О_Ревью и О_Мета-Ревью блокирующие.

НИС Современный ML (ИИ24, 7 модуль) - История изменений

imported>Mmustafaeva: Добавление информации о курсе