Advanced Machine Learning
This course introduces and discusses advanced topics in machine learning. Students will delve into the role of pattern analysis and probabilistic modeling, together with the mathematical techniques that will enable them to be able to solve real-world machine learning tasks: from data to inference. Topics Included: Statistical Machine Learning Theory, Analysis and Evaluation of Statistical Models, Analysis of Data, Supervised Learning - Artificial Neural Networks, Supervised Learning - Kernel Methods, Unsupervised Learning – Clustering, Unsupervised Learning - Topic Modeling, Feature Engineering, Missing Data, Basic Reinforcement Learning, Basic Semi-Supervised Learning.
- Yoshua Bengio, Learning Deep Architectures for AI, 2005
- Csaba Szepesvari, Algorithms for Reinforcement Learning, 2009
- David Barber, Bayesian Reasoning and Machine Learning, 2014
- Ian H. Witten & Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2005
- Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, Deep Learning, 2015