Robotics and Applied Machine Learning

Recommended Prerequisites

NA

Teaching Methods

The course is a face-to-face format, where the lectures (T) will cover key concepts, theories, techniques and algorithms related to mobile robotics and machine learning. The practical lectures (PL), an important part of the course, will provide opportunities to consolidate what has been learned during the lectures (T). The practical work (ie, coursework) will be done under the supervision of the Lecturer and/or teaching assistant(s). Marking (ie, grading) will be based essentialy on 2 components: (i) a short report or a concise project related to a practical problem; (ii) short presentations. The classification/scores will be attributed on a qualitative scale with 4 levels of approval (according to the non-degree courses practices).

Learning Outcomes

The main objective is for students (bachelors, master or early-PhD) to understand the fundamentals (both theory and practical) of mobile robotics, automated systems and machine learning and their applications to real-world problems.

Work Placement(s)

No

Syllabus

Module 1. Introduction and overview of mobile robotics, automated systems, machine learning (ML) applied to robotics, autonomous driving, probabilistic decision, automation

Module 2. Fundamentals of mobile robotics

Module 3. Mobile robotics hardware and systems

Module 4. Mobile robotics programming/coding

Module 5. Robot Operating System (ROS)

Module 6. Probability and statistics, Bayesian inference, Bayesian Networks (BN)

Module 7. Pattern recognition, principles, algorithms, performance measures

Module 8. Linear and non-linear regression

Module 9. Deep-learning, advanced techniques

Module 10. Kalman filtering, fuzzy-systems

Module 11. Case studies related to Robotics, autonomous driving, automation

Module 12. ML in practice, TensorFlow (or equivalent tools), case studies, pattern recognition, object detection

Head Lecturer(s)

Cristiano Premebida

Assessment Methods

Assessment
Short report or a concise project related to a practical problem and Short presentations: 100.0%

Bibliography

- Sebastian Thrun, Wolfram Burgard, Dieter Fox. “Probabilistic robotics”, MIT Press, (2006)

- Christopher M. Bishop "Pattern Recognition and Machine Learning". Springer (2006)

- Roland Siegwart, et al. “Introduction to Autonomous Mobile Robots”, Second Edition (2011)

- Shalom, B. “Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software”. Wiley. (2001)

- Kevin P. Murphy "Machine Learning: a Probabilistic Perspective", the MIT Press (2012)

- A. Papoulis, S.U. Pillai. “Probability, Random Variables and Stochastic Processes”, (2002)

- Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie. "The Elements of Statistical Learning", (2009)

- David Barber "Bayesian Reasoning and Machine Learning", Cambridge University Press (2012)

- Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press (2016)