- Aurlien Gron. 2017. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st ed.). O'Reilly Media, Inc.
- Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
- Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.
- Witten, Ian H., Frank, Eibe & Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques (3rd ed.) Burlington, MA: Morgan Kaufmann.
• A collection of scientific articles will bed added to the above mentioned literature.
Advanced Machine Learning
About the course
The purpose of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise him or herself with recent developments in this field.
Course content
The course contains the following elements:
- Data transformation, Data Augmentation, adjustment/calibration of model parameters (including Advanced Feature Extraction, Hyper-parameter Optimisation)
- Interactive machine learning methods (including Human-Machine Collaboration, Active Learning, Online learning, Incremental Learning, Learning from Data Streams)
- Meta-learning algorithms and Ensemble Methods
- Advanced algorithms for supervised learning and unsupervised learning (with emphasis on discriminative and generative Deep Learning architectures)
- Reinforcement learning (including Policy Search, Policy Iteration, Value Iteration, Q-learning)
- Trends and current front line research in machine learning
Entry requirements and selection
Entry requirements
1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
2. Knowledge equivalent to English 6 at Swedish upper secondary level.
3. At least 15 credits in programming.
4. At least 7.5 credits in mathematics.
5. Passing grade in the course Statistical methods for Data Science (MA660E)
Participation in the course also requires knowledge obtained from the course Artificial intelligence for Data Science (DA631E).
Selection
100% University credits completed
Course literature
Course evaluation
The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).
