- Data Pre-processing & Exploration - data - feature selection, dimensionality reduction (linear and non linear)
- Text categorization, word embeddings, deep learning for text
- Graph Based Text Mining
- Deep Learning for NLP 1
- Deep Learning for NLP 2
- Graph Similarity; Kernels ; ML for graphs
- Deep Learning for Graphs 1
- Deep Learning for Graphs 2
- Graph based recommendations
REFERENCES
–Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt
–Pattern Recognition and Machine Learning (Information Science and Statistics) Hardcover – October 1, 2007, Christopher M. Bishop
We
will give an overview of algorithmic aspects of Robotics. We will cover
typology of robots and give an overview of the most common sensors (vision, 3D
ultrasound, accelerometers, odometry) and present the principle of the
Perception-Decision-Action loop, and its various possible architectures
(hierarchical vs reactive in particular); We will also cover the various
navigation components: control; obstacle avoidance; localization; mapping and
planning along with filtering techniques
(Kalman filter, particle filtering etc
...) used in these areas.
- 教师: FILLIATDavid
- 教师: MOUTARDE Fabien
- 教师: YANG Ming
- 教师: 陆佳亮