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In this course, you will learn:
- knowledge of the fundamentals of machine learning and scientific modeling
- A working understanding of deep learning models such as MLPs, CNNs, and RNNs
- Knowledge of classic machine learning methods such as regression, SVMs, and decision trees
- An grasp of how to use these methods correctly
- Hands-on experience using sklearn and Keras to implement various machine learning models
Syllabus :
- Scientific Programming with Python
- Machine Learning with Sklearn
- Neural Networks and Keras
- Regression and Optimization
- Basic Probability Theory
- Probabilistic Regression and Bayes Nets
- Generative Models
- Cyclic Models and Recurrent Neural Networks
- Reinforcement Learning
- Artificial intelligence, the Brain, and Our Society
