Teaching

Timetable:

    Lectures: 30 hours, tutorials: 10 hours, labs: 10 hours.

    Sep. 11 - Dec. 1, 2023 (on campus)

    lectures (all weeks): Tuesdays noon-2pm, GLA.XG21.

                        (weeks 4,6,8,10): Mondays noon-1pm. GLA.QG21.

    tutorials (weeks 2-10): Fridays 11am-1pm, GLA.C104, delivered by Mr. Waqar Ahmad.

    labs (weeks 3, 5, 7, 9, 11): Wednesdays 4-6pm, GLA.L101.

Module description:

    MS226 aims to provide students with an introduction to the basics of statistics, including the use of  common discrete and continuous distributions, central limit theorem, sampling techniques as well as estimation and hypothesis testing techniques. Practical examples will be provided throughout using R.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    S. Ross, A first course in probability (8th ed), 2010.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, Dec. 2023) 70%,

    Lab test in R (1.5h, week 10) 30%.

   Resits available for both components (in August 2024).

Timetable:

    Lectures: 30 hours. Labs: 12 hours.

    Jan. 16 - Apr. 14, 2023 (on campus).

Module description:

    Deep learning is an emerging branch of machine learning that focuses on learning appropriate data representations from the data itself rather than operating with respect to a predefined model. In the first half of this course we will explore the fundamental techniques and approaches of machine learning that led to the development and success of deep learning. These include stochastic gradient decent, random forests, perceptron, support vector machines and naïve Bayes methods. After that artificial neural networks and the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and more complex convolutional neural networks. We will address the special type of deep learning architectures that is suitable for time series analysis: recurrent neural networks (RNNs), and in particular, a special type of RNNs - long short-term memory (LSTM) networks. The presentation will focus on actuarial and financial applications to ensure practical understanding of the critical machine learning and deep learning concepts such as representation, generalization, overfitting and model architecture. The lectures will be supported by computer labs demonstrating the use of the proposed methodologies using R / Keras programming language.

Course materials:

    Lecture notes and lab materials distributed via Loop.

References:

    Coursera Machine Learning course [link] - free and very well developed introductory course

    Coursera Deep Learning & Neural Networks course [link] - free introductory-level crash course in deep neural networks, Python-based.

    B. Lantz, Machine Learning with R, 2013.

    F. Chollet, J. Allaire, Deep Learning with R, 2018. [free_Chapter1]  [free_Chapter2]  [free_Chapter3]  

    I. Goodfellow, Y. Bengio, A. Courville, F. Bach, Deep Learning (Adaptive Computation and Machine Learning Series), 2017 [link]. More advanced reading, better state-of-the-art.

Exam/Assessment:

    Continuous assessment 100% - 3 home assignments (based on R programming language).

Timetable:

    Lectures: 30 hours, tutorials: 10 hours, labs: 12 hours.

    Jan. 16 - Apr. 14, 2023 (on campus).

Module description:

    MS228 aims to provide a strong foundation in the fundamental statistical method of regression modelling. Simple and multiple linear regression models will be presented. The fitting and interpretation of regression models will be explained and practical examples given. The linear model will be extended to model non-normal data using generalised linear models (GLMs). The regression models will be applied to practical datasets using R. Students will also be introduced to Bayesian statistical methods and their use in credibility theory.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, May 2023) 70%,

    Lab test in R (1.5h, week 12) 30%.

    Both components must be passed successfully (resits available in August).

Timetable:

    Lectures: 30 hours, tutorials: 10 hours, labs: 12 hours.

    Sep. 12 - Dec. 2, 2020 (on campus)

Module description:

    MS226 aims to provide students with an introduction to the basics of statistics, including the use of  common discrete and continuous distributions, central limit theorem, sampling techniques as well as estimation and hypothesis testing techniques. Practical examples will be provided throughout using R.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    S. Ross, A first course in probability (8th ed), 2010.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, Dec. 2022) 70%,

    Lab test in R (1.5h, week 10) 30%.

    Both components must be passed successfully (resits available in August).

Timetable:

    Lectures: 24 hours. Labs: 12 hours.

    Jan. 10 - Apr. 2, 2022

    lectures (all weeks): Wednesdays 11am-1pm @ Q120.

    labs (weeks 2, 4, 6, 8, 10, 12): Mondays 11am-1pm @ L101.

Module description:

    Deep learning is an emerging branch of machine learning that focuses on learning appropriate data representations from the data itself rather than operating with respect to a predefined model. In the first half of this course we will explore the fundamental techniques and approaches of machine learning that led to the development and success of deep learning. These include stochastic gradient decent, random forests, perceptron, support vector machines and naïve Bayes methods. After that artificial neural networks and the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and more complex convolutional neural networks. We will address the special type of deep learning architectures that is suitable for time series analysis: recurrent neural networks (RNNs), and in particular, a special type of RNNs - long short-term memory (LSTM) networks. The presentation will focus on actuarial and financial applications to ensure practical understanding of the critical machine learning and deep learning concepts such as representation, generalization, overfitting and model architecture. The lectures will be supported by computer labs demonstrating the use of the proposed methodologies using R / Keras programming language.

Course materials:

    Lecture notes and lab materials distributed via Loop.

References:

    Coursera Machine Learning course [link] - free and very well developed introductory course

    Coursera Deep Learning & Neural Networks course [link] - free introductory-level crash course in deep neural networks, Python-based.

    B. Lantz, Machine Learning with R, 2013.

    F. Chollet, J. Allaire, Deep Learning with R, 2018. [free_Chapter1]  [free_Chapter2]  [free_Chapter3]  

    I. Goodfellow, Y. Bengio, A. Courville, F. Bach, Deep Learning (Adaptive Computation and Machine Learning Series), 2017 [link]. More advanced reading, better state-of-the-art.

Exam/Assessment:

    Continuous assessment 100% - 3 home assignments (based on R programming language).

Timetable:

    Lectures: 30 hours, tutorials: 10 hours, labs: 10 hours.

    Jan. 10 - Apr. 2, 2022

    lectures (all weeks): Thursdays 11am-1pm @ Q120.

    tutorials (weeks 2-10): Tuesdays 1-3pm @ HG18, delivered by Mr. Ran Li.

    labs (weeks 3, 5, 7, 9, 11): Tuesdays 3-5pm, @ LG25 / synchronous (zoom).

Module description:

    MS228 aims to provide a strong foundation in the fundamental statistical method of regression modelling. Simple and multiple linear regression models will be presented. The fitting and interpretation of regression models will be explained and practical examples given. The linear model will be extended to model non-normal data using generalised linear models (GLMs). The regression models will be applied to practical datasets using R. Students will also be introduced to Bayesian statistical methods and their use in credibility theory.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, May 2022) 70%,

    Lab test in R (1.5h, week 12) 30%.

    Both components must be passed successfully (resits available in August).

Timetable:

    Lectures: 30 hours, tutorials: 10 hours, labs: 10 hours.

    Sep. 20 - Nov. 26, 2021 (on campus)

Module description:

    MS226 aims to provide students with an introduction to the basics of statistics, including the use of  common discrete and continuous distributions, central limit theorem, sampling techniques as well as estimation and hypothesis testing techniques. Practical examples will be provided throughout using R.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    S. Ross, A first course in probability (8th ed), 2010.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, Dec. 2021) 70%,

    Lab test in R (1.5h, week 10) 30%.

    Both components must be passed successfully (resits available in August).

Timetable (all classes remote):

    Lectures: 24 hours. Labs: 12 hours.

    Jan. 18 - Apr. 16, 2021

    lectures (all weeks): Mondays 2-4pm, synchronous (zoom).

    labs (weeks 2, 4, 6, 8, 10, 12): Thursday 2-4pm, synchronous (zoom).

Module description:

    Deep learning is an emerging branch of machine learning that focuses on learning appropriate data representations from the data itself rather than operating with respect to a predefined model. In the first half of this course we will explore the fundamental techniques and approaches of machine learning that led to the development and success of deep learning. These include stochastic gradient decent, random forests, perceptron, support vector machines and naïve Bayes methods. After that artificial neural networks and the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and more complex convolutional neural networks. We will address the special type of deep learning architectures that is suitable for time series analysis: recurrent neural networks (RNNs), and in particular, a special type of RNNs - long short-term memory (LSTM) networks. The presentation will focus on actuarial and financial applications to ensure practical understanding of the critical machine learning and deep learning concepts such as representation, generalization, overfitting and model architecture. The lectures will be supported by computer labs demonstrating the use of the proposed methodologies using R / Keras programming language.

Course materials:

    Lecture notes and lab materials distributed via Loop.

References:

    Coursera Machine Learning course [link] - free and very well developed introductory course

    Coursera Deep Learning & Neural Networks course [link] - free introductory-level crash course in deep neural networks, Python-based.

    B. Lantz, Machine Learning with R, 2013.

    F. Chollet, J. Allaire, Deep Learning with R, 2018. [free_Chapter1]  [free_Chapter2]  [free_Chapter3]  

    I. Goodfellow, Y. Bengio, A. Courville, F. Bach, Deep Learning (Adaptive Computation and Machine Learning Series), 2017 [link]. More advanced reading, better state-of-the-art.

Exam/Assessment:

    Continuous assessment 100% - 3 home assignments (based on R programming language).

Timetable (all classes remote):

    Lectures: 30 hours, tutorials: 10 hours, labs: 10 hours.

    Jan. 18 - Apr. 16, 2021

    lectures (all weeks): Tuesdays 2-4pm, synchronous (zoom).

    tutorials (weeks 2-10): Wednesdays 2-3pm, synchronous (zoom), delivered by Mr. Ran Li.

    labs (weeks 3, 4, 7, 9, 11, 13): Thursdays 10am-noon, synchronous (zoom).

Module description:

    MS228 aims to provide a strong foundation in the fundamental statistical method of regression modelling. Simple and multiple linear regression models will be presented. The fitting and interpretation of regression models will be explained and practical examples given. The linear model will be extended to model non-normal data using generalised linear models (GLMs). The regression models will be applied to practical datasets using R. Students will also be introduced to Bayesian statistical methods and their use in credibility theory.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, May 2021) 70%,

    Lab test in R (1.5h, week 13) 30%.

    Both components must be passed successfully (resits available in August).

Timetable (all classes remote):

    Lectures: 30 hours, tutorials: 10 hours, labs: 10 hours.

    Oct. 5 - Dec. 11, 2020

    lectures (all weeks): Friday 10am-noon, synchronous (zoom).

    tutorials (weeks 2-10): Thursday 2-3pm, synchronous (zoom), delivered by Mr. Ran Li.

    labs (weeks 3, 5, 7, 9, 10): Tuesdays 10am-noon, synchronous (zoom).

Module description:

    MS226 aims to provide students with an introduction to the basics of statistics, including the use of  common discrete and continuous distributions, central limit theorem, sampling techniques as well as estimation and hypothesis testing techniques. Practical examples will be provided throughout using R.

Course materials:

    Lecture slides, tutorials and lab materials distributed via Loop.

References:

    S. Ross, A first course in probability (8th ed), 2010.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Written exam (2h, Jan. 2021) 70%,

    Lab test in R (1.5h, week 10) 30%.

    Both components must be passed successfully (resits available in August).

Timetable:

    Lectures/labs: 36 hours.

    Jan. 27 - Apr. 17, 2020

    lectures (all weeks): Tuesday 10-11am, Glasnevin CG86 / online from 16/03/20.

    lectures/labs (all weeks): Thursday 2-4pm, Glasnevin H102 / online from 16/03/20.

Module description:

    Deep learning is an emerging branch of machine learning that focuses on learning appropriate data representations from the data itself rather than operating with respect to a predefined model. In this course we will explore the fundamental elements and models of deep learning used for financial applications. The latter include portfolio management, social & news analysis as well as automated extraction of information from other non-financial sources in order to inform sophisticated AI-based financial predictions. We will start with fundamental machine kerning methods, including logistic regression, ensemble and classification techniques. Then the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and convolutional neural networks. We will address the special type of deep learning architectures that is suitable for time series analysis: recurrent neural networks (RNNs), and in particular, a special type of RNNs: long short-term memory (LSTM) networks. The presentation will focus on financial applications and practical understanding of the critical deep learning concepts such as generalization, overfitting and model architecture. The course will include labs and rely on the use of R programming language and Keras for neural networks modeling.

Course materials:

    Lecture notes and lab materials distributed via Loop.

References:

    Coursera Machine Learning course [link] - free and very well developed introductory course

    Coursera Deep Learning & Neural Networks course [link] - free introductory-level crash course in deep neural networks, Python-based.

    B. Lantz, Machine Learning with R, 2013.

    F. Chollet, J. Allaire, Deep Learning with R, 2018. [free_Chapter1]  [free_Chapter2]  [free_Chapter3]  

    I. Goodfellow, Y. Bengio, A. Courville, F. Bach, Deep Learning (Adaptive Computation and Machine Learning Series), 2017 [link]. More advanced reading, better state-of-the-art.

Exam/Assessment:

    Continuous assessment 100% - 3 home assignments (based on R programming language).

Timetable:

    Lectures: 24 hours, tutorials: 10 hours, labs: 8 hours.

    Jan. 27 - Apr. 17, 2020

    lectures (all weeks): Friday 11am-1pm, Glasnevin Q119 / online from 16/03/20.

    tutorials (all weeks): Thursday 10-11am, Glasnevin X130 / online from 16/03/20.

    labs (weeks 3, 6, 9, 12): Wednesday 2pm-4pm, Glasnevin H102 / online from 16/03/20.

Module description:

    MS258. Statistics II, introduces students to the core statistical techniques of sampling, parameter estimation, confidence interval generation, hypothesis testing, linear regression and ANOVA. MS258 builds on the fundamental statistics covered in MS255.

Course materials:

    Lecture notes, tutorials and lab materials distributed via Loop.

References:

    D. Crawshaw and J. Chambers, A Concise Course in Advanced Level Statistics, (4th ed.), 2001.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

    P. Dalgaard, Statistics with R, (2nd ed.), 2008.

Exam/Assessment:

    Exam 80%, written examination on May 12, 2020, 9.30-11.30am; 

    Lab assignment in R (week 12) 20%.

Timetable:

    Lectures: 24 hours, tutorials: 10 hours, labs: 8 hours.

    Sep. 23 - Dec. 13, 2019

    lectures (all weeks): Tuesday 3-5pm, Glasnevin CG86.

    tutorials (weeks 2-4, 6-12): Thursday 3-4pm, Glasnevin CG04.

    labs (weeks 3, 6, 9, 12): Friday 11am-1pm, Glasnevin H102.

Module description:

    MS255 aims to provide students with a strong foundation in statistics. Students will become familiar with common probability distributions, their generation and application. The theory will be extended to cover joint distributions and conditional expectations. Students will also be introduced to the Central Limit Theorem.

Course materials:

    Lecture notes, tutorials and lab materials distributed via Loop.

References:

    S. Ross, A first course in probability (8th ed), 2010.

    G. Grimmett and D. Welsh, Probability: an introduction (2nd ed.), 2014.

Exam/Assessment:

    Exam 80%, written examination on January 17, 2020, 9.30-11.30am;

    In-class test (week 8) 10%; 

    Lab assignment in R  (week 12) 10%.

Main Lecturer:

    Prof. Rozenn Dahyot [link].

    Course page [link].

Invited Lectures:

    18/10/2018 Classification and Ensemble Learning (1h)

    14/11/2018 Deep Learning Architectures for Computer Vision (1h)

    13/11/2019 Deep Learning Architectures for Computer Vision (1h)

    18/11/2020 Deep Learning Architectures for Computer Vision (1h)

Timetable:

    Lecture hours: 27, tutorial hours: 6

    Sep. 27 - Dec. 15, 2017

    Wednesday 3-4pm, Lect. Salmon.

    Friday 1-3pm, LB08.

Module content:

    Events and probabilities, probability spaces

    Independence and conditional probability

    Discrete and continuous random variables

    Multivariate distributions

    Moment generating and characteristic functions 

    The law of large numbers and the central limit theorem 

    Tutorials: [T1] [T2] [T3] [T4] [T5] 

    Exam: [Sample_Exam] [Sample_Exam_Answers] [Past_Exams]

References:

    Lecture notes for this course prepared by Prof. Simon Wilson.

    Probability: an introduction (2nd ed.) - by G. Grimmett and D. Welsh [amazon].

    An Introduction to Probability Theory and Its Applications. Volume 1 (3rd ed.) - by W. Feller [amazon].

Exam/Assessment:

    Exam 100%, written examination to be held on May 14, 2018, from 2-4pm.