COMP9318 will be offered in 2017s1 with on-going revision to incorporate popular and latest techniques in Data Science.
As such, the course
- has a broad coverage of topics,
- uses (non-trivial) python and Jupyter (ipython notebook), and
- requires students to have a good grasp and fluency of maths (analytics, linear algebra, probability and statistics, etc.) and algorithm design/analysis (advanced data structures, recursion, dynamic programming, etc.).
A gentle reminder: the course tends to have a fairly high workload and fail rate.
Motivated and highly competant students also have the opportunity to work on a research-oriented project instead of the usual assessments. Please contact LiC (Dr. Wei Wang, weiw@cse) ASAP to discuss the details and obtain the approval. The successfully conducted research projects in 2016s2 are:
- A. OLDONI, Knowledge graph construction for research literatures:
- J. S. CHOPRA, Combinational Collaborative Filtering: An Approach For Personalised, Contextually Relevant Product Recommendation Baskets:
- Tackles a real and challenging data mining problem (due to the combinatorial nature); achieving a comparable accuracy as human assessors.