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Course Outline
Advanced Neural Networks
- Deep learning architectures.
- Convolutional and recurrent neural networks.
- Generative models and unsupervised learning.
Machine Learning at Scale
- Big data analytics.
- Distributed computing for ML.
- Advanced optimization techniques.
Reinforcement Learning and Decision Making
- Markov decision processes.
- Policy gradient methods.
- Multi-agent systems and game theory.
Natural Language Processing and Understanding
- Advanced NLP techniques.
- Sentiment analysis and text classification.
- Language models and transformers.
Computer Vision and Perception
- Image recognition and object detection.
- Video analysis and action recognition.
- 3D reconstruction and augmented reality.
AI Ethics and Society
- Bias and fairness in AI systems.
- AI governance and policy.
- Future societal impacts of AI.
Lab Project
- Implementing advanced ML models.
- Analyzing large datasets.
- Collaborating on a group research project.
Summary and Next Steps
Requirements
- A strong grasp of fundamental AI and ML concepts.
- Proficiency in Python and familiarity with data science toolkits.
- Completion of an introductory AI course or equivalent practical experience.
Audience
- Data scientists.
- Engineers.
- AI practitioners.
21 Hours
Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
