- A Not-So-Gentle Introduction to Machine Learning (Last revised on 08/09/2017). This is a short guide on some of the most used machine learning techniques. The guide is divided into two parts: (i) Supervised learning, where both regression and classification are tackled by introducing, inter-alia, probabilistic and non probabilistic linear regression models in the context of both the Maximum Likelihood and the Bayesian framework, K-NN, CART, and Support Vector Machines (ii) Unsupervised learning, where both clustering and dimensionally reduction are talked by introducing probabilistic (Gaussian Mixture Models) and non probabilistic (K-Means) clustering, Principal Component Analysis, and Multidimensional Scaling.
These are lecture notes for courses that I have taught.
- The R Course (Last revised on 13/03/2017). These are the lecture notes that I have used to teach the R Course for the Warwick Morse Society in the 2016/2017 academic year. They cover the following topics in R Programming: (i) Operator Syntax; (ii) Data Structures; (iii) Subsetting; (iv) Control Structures; (v) Functions; (vi) Import Export; (vii) Graphics; (viii) Functional Programming; (ix) Functionals.
These are the materials for the workshops that I have organised.
- Of Neural Networks And Kernel Machines (University of Warwick 19/07/2017 and 26/07/2017). This was a two-day workshop which covered theoretical and practical aspects of Feedforward Neural Networks, Support Vector Machines and Support Vector Regression as well as how to train them in Python. The presentations, practicals and accompanying notes are all available for download below:
- Del Vecchio, M. (2017) The Michelin Curse: Expert Judgment Versus Public Opinion Presentation at the International Conference Of Undergraduate Research (ICUR). Slides available here.
These are revision notes for modules taught at the Univeristy of Warwick.
- ST301 Bayesian Statistics and Decision Theory (Last revised on 17/04/2017). This notes cover the following concepts in Bayesian Statistics and Decision Theory: (i) Decision Trees; (ii) Utility Theory; (iii) Extensive and Normal Form Analysis Of A Decision Problem; (iv) Sensitivity And Probability; (v) Bayesian Networks And Relevance.
- EC220 Mathematical Economics 1A (Last revised on 1/06/2016). This notes cover the following topics in Game Theory: (i) Static Games of Complete Information; (ii) Dynamic Games of Complete Information; (iii) Static Games of Incomplete Information; (iv) Dynamic Games of Incomplete Information; (v) Evolutionary Game Theory.
- IB132 Foundations of Finance (Last revised on 31/05/2016). This notes cover the following concepts in Finance: (i) Present Value; (ii) Perpetuities and Annuities; (iii) Capital Budgeting; (iv) Bonds; (v) Uncertainty Default and Risk; (vi) Uncertainty Bonds and Equity; (vii) Risk and Reward; (viii) The Capital Asset Pricing Model (CAPM); (ix) Complications in Capital Budgeting; (x) Capital Structure in Perfect Markets; (xi) Capital Structure in Imperfect Markets; (xii) Equity Payout; (xiii) Option Contracts.