Machine Learning I
This course provides a broad introduction to machine learning, data mining and statistical pattern recognition. It will also discuss recent applications of machine learning such as robotics control, autonomous navigation, bioinformatics, text and web processing and speech recognition.
Topics to be covered include; Learning theory; Basics concepts of machine learning; Generative learning algorithms; Evaluating and debugging learning algorithms; Bias/variance tradeoff and VC dimension; Value and policy iteration; Q-learning and value function approximation
- Ian Goodfellow, Yoshua Bengio & Aaron Courville, Deep Learning
- Christopher M. Bishop, Pattern Recognition and Machine Learning
- Tom M. Mitchell, Machine Learning
- Patrick Hebron, Machine Learning for Designers
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).