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Data Science

Current topics in the theory of neural networks: Dynamics and Data

Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.

Machine learning applications to single-cell genomics

Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.

Current topics in the theory of neural networks: Dynamics and Data

Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.

Scientific Computing and Algorithms

Each bloc requires 6 hrs of lectures and 3 hrs of Labs = 9 x 4 = 36 hours (Dates TBC)

Bayesian Inference II

Each bloc requires 6 hrs; 24 lectures + 4 x 3 hrs labs = 36 hours (5 weeks: 11/01-24/02)

Neural Networks

12x2h lectures + 4x3hrs labs = 36 hours (6 weeks: 25/01 - 05/03)

Introduction to Statistical Modelling and Inference

Each topic requires 4 hours = 20 hours + 2 x 3hrs Lab = 26 hours (4 weeks: 05/10-30/10)

Pre-requisites: familiarity with Python and jupyter installed on students’ computers.

Information Theory and Inference

10 x 2 hours + 2 x 3 hrs Labs = 26 hours (5 weeks: 04/11 - 04/12)

Bayesian inference I

Each lecture requires 2 hrs; 12 lectures + 4 labs = 36 hours (7 weeks: 19/10 - 4/12)

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