This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.
This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.
The lectures cover fundamental methodological and theoretical principles while computer workshops provide students with an opportunity to gain hands-on-experience by solving exercises using modern and commonly used software libraries such as PyTorch and OpenAI Gym. This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours across Lent Term. This year, some of this teaching may be delivered through a combination of classes and flipped-lectures delivered as short online videos. Students are required to use a Python programming environment, e.g. by installing Anaconda / Jupyter notebooks on their own laptops or using Google Colab, and to use their own laptops in the workshops.
Students will be expected to produce 5 exercises in the LT.
1. I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2017, http://www.deeplearningbook.org2. R. Sutton and A. C. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 20183. A. Zhang, Z. Lipton, M. Li and A. Smola, Dive into Deep Learning, 2020, http://d2l.ai4. M. Nielsen, Neural Networks and Deep Learning, 2016, online book.5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson, 20166. OpenAI Gym documentation, http://gym.openai.com/docs/
Coursework (30%) in the LT. Project (70%) in the ST. Students are required to hand in the solutions to 3 sets of exercises (each accounting for 10% of the final grade). The project will be a group project with 2 members per group. The detailed instruction will be handed out in Week 10 of Lent term, and students need to submit a written report by Week 1 of Summer term.