Course Outline
소개
확률 이론, 모델 선택, 의사결정 및 정보 이론
확률 분포
회귀 및 분류를 위한 선형 모델
Neural Networks
커널 방법
희소 커널 머신
그래픽 모델
혼합 모델 및 EM
대략적인 추론
샘플링 방법
연속 잠재변수
순차적 데이터
모델 결합
요약 및 결론
Requirements
- 통계의 이해.
- 다변량 미적분학 및 기본 선형 대수학에 익숙합니다.
- 확률에 대한 경험이 있습니다.
청중
- 데이터 분석가
- 박사 과정 학생, 연구원 및 실무자
회원 평가 (5)
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course - Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.