STUDENT HANDBOOK, Study notes of Engineering

Download STUDENT HANDBOOK and more Study notes Engineering in PDF only on Docsity! School of Computer Science College of Science and Engineering School of Computer Science, IT Building, NUI Galway STUDENT HANDBOOK 2021 | 2022 Page 2 of 22 NUI Galway School of Computer Science requires all students to have exclusive use of a laptop for use in lectures and labs, for home use of online materials and for participation in online sessions. The minimum and recommend spec are detailed at http://www.nuigalway.ie/science- engineering/school-of-computer-science/currentstudents/laptops/. We also operate a laptop loan scheme for students who cannot afford a suitable laptop (see same address). Page 5 of 22 Section 2: General Information 2.1 Academic Calendar 2021-2022 For the latest version visit http://www.nuigalway.ie/registry/academic-term-dates/ Academic Year 2021-2022 Semester 1 Start of Teaching (UG years (excluding Year 1) and Postgraduate Taught programmes) Monday 6th September 2021 End of Teaching all years Friday 26th November 2021 Semester 1 Exams Monday 6th December – Friday 17th December 2021 Semester 2 Teaching (All Years, UG & PGT) Monday 10th January – Friday 1st April 2022 Easter Good Friday 15th April - Easter Monday 18th April 2022 Field Trips Monday 4th April – Friday 8th April 2022 Study Week (All Years, UG & PGT) Monday 11th April – Friday 15th April 2022 Semester 2 Exams Tuesday 19th April – Friday 6th May 2022 Autumn Repeat Exams Tuesday 2nd August to Friday 12th August 2022 Easter Holidays: Good Friday 15th April to Easter Monday 18th April 2022 Bank Holidays: Monday 25th October 2021 / Thursday 17th March 2022 / Monday 2nd May 2022 / Monday 6th June 2022 / Monday 1st August 2022 Page 6 of 22 2.2 Key Contact Details Programme Director Programme Programme Director Room E:mail M.Sc in Computer Science – Artificial Intelligence Dr Matthias Nickles 411 matthias.nickles@nuigalway.ie Technical and Administrative Staff Administrative Staff Room E:mail Ms Deirdre King School Administrator 413 computerscience@nuigalway.ie IT Technical Staff Mr Peter O’Kane Chief Technical Officer 435 Peter.okane@nuigalway.ie Mr John Hynes Senior Technical Officer 420 John.hynes@nuigalway.ie Mr Joe O’Connell Senior Technical Officer 434 Joe.oconnell@nuigalway.ie The School of Computer Science is located in the IT Building, Floor 3. For directions to the IT Building please click here.   Page 7 of 22 Student Contact Centre The Student Contact Centre (tel: (091) 495999) provides the following services and is located on the ground floor of Áras Uí Chathail, which is situated on the main campus: • Registration, Exams and Admissions queries • Prospectus pick up • Replacement ID Cards • Transcript Requests • Validation and stamping of forms, e.g., social welfare, medical card, drug payment, • USIT visa (Student Travelcard forms are stamped by SU) • Change of Name/ Change of Address requests • Statements, e.g., letters of attendance Useful Contact Numbers (for up-to-date contact details and email addresses visit http://www.nuigalway.ie/about-us/contact-us/) Student Information Desk (091) 495999 ISS Help Desk (091) 495777 Admissions Office (091) 495999 Accommodation Office (091) 492760 Disability Liaison Office (091) 492813 Fees Office (091) 492386 Health & Safety Office (091) 492678 Campus Security / Emergency (091) 493333 Student Counselling (091) 492484 Student Health Unit (091) 492604 Students Union Shop (091) 492411 General Emergency 999 Local Garda Station (091) 538 000 Hospital (UCHG) (091) 580580 Samaritans (091) 561222 2.3 Maps NUI Galway Campus map can be located on the University’s website at: http://www.ptba.nuigalway.ie/images/campus_map_.pdf Page 10 of 22 2.6 International Students The International Office provide advice, information and support service for all International Students. For incoming international students information is available on http://www.nuigalway.ie/international-students/ The latest Covid-19 related information for international students can be found at http://www.nuigalway.ie/international-students/covid-19/ Also frequently visit http://www.nuigalway.ie/alert/ for up-to-date information. All international students are strongly encouraged to attend English for Academic Purposes (EAP) classes which are specifically designed to equip international students with specific English skills to help them with their studies. Please refer to http://www.nuigalway.ie/international-students/ for more details. The International Student Officer, Ms. Louise Kelly may be contacted at International Office, National University of Ireland, Galway. Tel 353 91 493581, E-mail: louise.kelly@nuigalway.ie. Ms. Kelly acts as an identifiable point of contact with the various Student Services in the University to ensure that any problems of adjustment are minimised. She helps International Students adjust as quickly as possible to their new environment, so that they can derive maximum benefit and enjoyment from their life at NUI Galway. 2.7 Computer Science Account and Swipe Card Access to Labs The School of Computer Science has a number of undergraduate and postgraduate rooms which are for the use of our own students. Within these rooms are computers and printers. All students who are taking a module/course with the School of CS are entitled to an account to access the open access labs in the IT Building (Note: IT 106 is available to all NUIG students using main NUIG account). Depending on their course they may also have swipe card access to further project labs in the IT Building. Accounts are setup automatically after a student registers for one of our modules/courses, and students will receive an email to their NUIG email to indicate the account is ready for use. Students must then log on to a URL to retrieve their password: http://www2.it.nuigalway.ie/accounts/. This will give the password, weekly print quota and list any swipe card access to rooms. Students who have issues with their Computer Science computer account, a PC or swipe access in the IT Building should log a call to Computer Science Technical officers: support@it.nuigalway.ie. Useful link for further related info: http://www.cs.nuigalway.ie/currentstudents/. Students who have issues with their main NUIG account, WIFI, Blackboard, personal laptops or any PC/printer on the rest of campus should refer to the NUIG helpdesk: http://www.nuigalway.ie/information-solutions-services/services-for-students/ Page 11 of 22 2.8 DISC - Computer Programming Drop-In Support Centre Computer DISC is a Computer Programming Drop-In Support Centre for all NUI Galway students who are taking any programming/software development courses. The DISC is a free service that supports all students with their self-directed learning in computing topics at all years and levels in NUI Galway. The centre is located in Room 205 on 1st floor of the Information Technology (IT) Building. What services does Computer DISC provide to students? • Facilities for students to sit and work on programming problems • One-to-one advice and support for students, and focused small group tutorials • Books, courseware, web links, and other learning resources for programming students • A website with information and an email service for all queries • Advice for students who wish to learn new programming languages autonomously • Assistance with new technologies for project work such as Final Year Projects 2.9 Student Counselling The counselling service is part of a network of support services offered by NUI, Galway. It provides professional counselling, which is free and confidential to all students of NUI, Galway. Life as a student is exciting and challenging, an achievement usually gained after much hard work and preparation. It can also be stressful at times. You may find you are experiencing personal difficulties which are affecting your ability to study and to take full advantage of the opportunities available to you at NUI, Galway. This is where we can help. We are a team of qualified and experienced counsellors, psychologists and psychotherapists. The service operates within the Code of Ethics and Practice agreed by the Irish Association of University and College Counsellors (IAUCC). The services provided include: • Pre-counselling assessment • Individual counselling and psychotherapy • Group work • Information and referral • A consultation service for those who may have concerns about a student – such as tutors, university staff, friends or parents A drop-in service is open every weekday in term time from 2.00pm to 4.00pm. Further information is available http://www.nuigalway.ie/counsellors/ Contact Address: Counselling Services No. 5 Distillery Road NUI, Galway Direct Tel: 091 492484 ext. 2484 E-mail: counselling@nuigalway.ie Counselling Staff Ms. Geraldine Connolly, Head of Counselling Direct Tel: 091 – 495202, Ext. 5202 E-mail: geraldine.connolly@nuigalway.ie Ms. Emer Casey, Counsellor Direct Tel. 091 – 495633, Ext. 5633 E-mail: emer.casey@nuigalway.ie Page 12 of 22 2.10 Blackboard Blackboard is the Virtual Learning Environment (VLE) in use at NUI Galway. Blackboard is a web-based application that gives students access to all their courses at NUI Galway. Blackboard allows students to download lecture notes, reading lists, assessment information and other course-related material. Students have access to their online Blackboard courses once they have registered with NUI Galway. When a student registers for a course or module with the NUI Galway Student Records System, they are automatically enrolled in the corresponding course on Blackboard. These changes are recognised by Blackboard within 24 hours. If students require additional assistance with their login, they should contact the Service Desk within Information Solutions and Services (ISS) at https://www.nuigalway.ie/information- solutions-and-services/. ISS can assist students with queries they may have relating to Blackboard including logging in to Blackboard or queries relating to their password or e- mail account. If students are unable to see courses when they log into Blackboard, they will need to check their registration statement to ensure they are correctly registered. Students who are not registered, will need to contact Admissions or the Student Contact Centre on the ground floor of Áras Uí Chathail to process their registration details prior to gaining access to Blackboard. 2.11 Plagiarism Plagiarism refers to copying another author’s work without due reference or acknowledgement of the author. Plagiarism is not acceptable. It is essential that the candidate acknowledge other people’s work, when used by the student. The submitted work must be prepared by the candidate alone, and must be the result of the candidate’s own effort, skills and knowledge. It is unacceptable for candidates to knowingly permit others to copy their work. NUI Galway has a strict code of practice for dealing with plagiarism, please visit the following site for more details – http://www.nuigalway.ie/plagiarism/ 2.12 Information Solutions and Services (ISS) ISS aim to provide students with access to the ICT facilities which they need to succeed in their studies at NUI Galway. These facilities include high speed Internet access, an NUI Galway email account, and access to the resources of the James Hardiman Library and the Blackboard virtual learning environment. These services are accessible from the on-campus PC suites and from suitably equipped laptops using the on-campus wireless network. A Campus Account (CASS) provides students access using a single User ID and Password to all computing services, other than E-mail. Students should refer to their Registration Guide for their temporary activation password. To activate your Campus Account, student need to go one to https://cass.nuigalway.ie/ and login using their current student ID number and the activation password. ISS Service Desk (Service Desk is located in the foyer of the James Hardiman Library). E-mail address: servicedesk@nuigalway.ie Direct Phone: 091 495777. Extension: 5777 Page 15 of 22 2.15 Parking on Campus Parking spaces in NUI, Galway fall into a number of categories: • Staff Only • Student Only • Pay and Display (P&D) spaces • "Reserved" spaces and loading bays The parking permit payment system can be accessed via the Buildings Office website at http://www.nuigalway.ie/buildings/parking.html. Please note that you will be required to login, using your normal NUI Galway username/password authentication. To purchase/renew your Student Parking Permit please log on to https://nuig.apcoa.ie/applicant. If you park in a "Pay and Display" space, you must display a valid Pay & Display ticket and park only in spaces marked "Pay and Display". Should you have any queries, please consult our Frequently Asked Questions on http://www.nuigalway.ie/buildings/faqs/. A park and ride service operates from Dangan car park. Further information and timetable details are available from http://www.nuigalway.ie/media/buildingsoffice/files/services/parking/Park-and-Ride- Timetable-2018-v3.pdf Parking Office Location: Room 103A, Ground Floor Arts Millennium Building Opening Hours: Monday to Friday, 0930 - 1200 and 1400 - 1600 Contacts: Email: parking@nuigalway.ie Tel. 353 91 495063 (ext. 5063) 2.16 Registration Online registration opens on Thursday 19th August for postgraduate taught students. Details of relevant dates can be found on: http://www.nuigalway.ie/registration/quick-links/registration-dates/ Students will receive an email from registration before Online Registration opens inviting students to register online. Students should register as soon as possible for their programme to gain access to University services such as Student ID Card, Library, Blackboard, etc. Further registration details can be found on: http://www.nuigalway.ie/registration/how-to-register.html/newstudentpostgrad/ 2.17 Library The Student ID card also acts as a Library card. Students must have a current card in order to gain entrance to the Library. Details on the services provided by the library are available at http://library.nuigalway.ie/usingthelibrary/accessingthelibrary/ The Library and IT Service Desk is located on the ground floor of the library and provides advice and support to students on both Library and IT services (e.g., User ID/passwords, book loans, printing Wifi access). Page 16 of 22 2.18 Module Descriptions Modules – Semester I: 1MA1 Modules Core CT5165 Principles of Machine Learning (formerly CT4101 Machine Learning and CT475 Machine Learning and Data Mining) Credits: 5 Core CT5120 Introduction to Natural Language Processing Credits: 5 Core CT5132 Programming and Tools for AI Credits: 5 Core CT5142 Artificial Intelligence and Ethics Credits: 5 Core CT4100 Information Retrieval (formerly CT422 Modern Information Management) Credits: 5 Core CT5141 Optimisation Credits: 5 Optional EE445 Digital Signal Processing Credits: 5 Optional CT561 Systems Modelling and Simulation Credits: 5 Optional CT5105 Tools & Techniques for Large Scale Data Analytics Credits: 5 Note: In the event that students have previously taken one of these core or optional modules as a NUI Galway student, then you cannot enroll again in a module that you have previously been awarded credits for. Students that are affected can select from the other optional modules offered on the syllabus. Topics (changes possible. Details will be advised by the module lecturers): CT5165: Principles of Machine Learning (formerly CT4101, CT475) Definitions of Machine Learning, Data Mining and the relationship between them; the CRISP Data Mining process model; major tasks including classification, regression, clustering, association learning, feature selection, and reinforcement learning; algorithms for these tasks that may include decision tree learning, instance-based learning, probabilistic learning, support vector machines, linear and logistic regression, and Q-learning; open-source software tools for data mining; practical applications such as sensor data analysis, healthcare data analysis, and text mining to identify spam email; ethical issues and emerging trends in data mining and machine learning. Page 17 of 22 CT5120: Introduction to Natural Language Processing Introduction to natural language processing, including foundations in linguistics, statistical analysis and applications. CT5132 Programming and Tools for AI This module is about programming and computational tools required for artificial intelligence. It uses the Python language as the main vehicle, but focusses on conceptual material rather than just the language itself. It moves fast through introductory Python workings. It covers several important Python libraries in detail. It discusses approaches to building re-usable, high quality code but not on software engineering. It also visits some extra topics such as version control and an introduction to the R language for statistics. The module is core for the NUI Galway MSc in Artificial Intelligence (MSc AI) Part-time (online) and Full-time (classroom). The syllabus and assessment will be the same for both. We will use a hybrid of lecture-style and lab-style delivery. The lecture-style delivery will be via video (for the part-time/online version) or classroom (for the full-time version). Practical exercises will be interleaved with the lecture-style delivery. This module will be divided into the following main topics: 1. Weeks 1-2: Introductory Python: writing and executing Python code through an IDE, command line, or notebook; arithmetic; syntax; comments and doc-strings; variables; functions; loops and conditionals; lists, tuples, dictionaries; classes; input/output; version control. 2. Weeks 3-4: Python data libraries: Numpy, Pandas, Matplotlib, and friends. 3. Weeks 5-6: Introductory R: some side-by-side comparisons between R and Python; R for statistics. 4. Weeks 7-11: Python software for AI: Scikit-learn API (but not details of the algorithms themselves), NetworkX, and many examples. 5. Week 12: Testing, notebooks, cloud execution. We will use up-to-date versions of software, and in particular we will use Python 3 (not Python 2). CT5142 Artificial Intelligence and Ethics Artificial intelligence technologies have evolved dramatically in recent years, impacting on many areas of human life. Societal responses to these developments have ranged from Page 20 of 22 Modules – Semester II: 1MA1 Modules Core CT5100 Data Visualisation Credits: 5 Core CT5133 Deep Learning Credits: 5 Core CT5134 Agents, Multi-Agent Systems and Reinforcement Learning Credits: 5 Core CT5135 Research Topics in AI Credits: 5 Core CT5129 Artificial Intelligence Project (capstone project) Credits: 30 Optional CT5113 Web and Network Science Credits: 5 Optional EE551 Embedded Image Processing Credits: 5 Optional CT5121 Advanced Topics in Natural Language Processing Credits: 5 Optional CT5137 Knowledge Representation & Statistical Relational Learning Credits: 5 Note: In the event that students have previously taken one of these core or optional modules as a NUI Galway student, then you cannot enroll again in a module that you have previously been awarded credits for. Students that are affected can select from the other optional modules offered on the syllabus. Topics (changes possible. Details will be advised by the module lecturers): CT5100: Data Visualisation This module with teach the fundamentals of data visualization. It will cover basic design principles and the principles underlying human perception, color theory and narrative. It will focus on the use of open standards for the presentation of data on the Web such as HTML, CSS, SVG, JavaScript through the use of libraries such as D3.js, jQuery.js and Dimple.js. CT5133: Deep Learning This is an advanced module in machine learning, focusing on neural networks (NNs), deep NNs, and connectionist computing. Students learn about the basic principles and building blocks of deep learning, and how to implement a deep neural network ‘from scratch’. They also learning about software libraries and tools, and gain experience of applying deep Page 21 of 22 learning in a range of practical applications. The module includes substantial practical programming assignments. CT5134 Agents, Multi-Agents Systems and Reinforcement Learning The topic of Agents and Multi-Agent Systems, examines environment that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal. A typical agent environment could be a trading environment where an agent attempts to optimise energy usage, or the profitability of a transaction. More recently, significant global attention has focussed on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals. This module begins by examining the underpinnings of what is an Agent, and how we can better understand the principles of an agent and its autonomy. Multi-Agent Systems are then explored, as a means of understanding how many agents can interact with each other in a complex environment. Agents are commonly modelled using Game Theory, and in this module a range of Game Theoretic Models will be studied. The module will examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge. The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples in discrete environments and how to formulate a reinforcement learning task. It then extends this to continuous problem spaces, detailing Deep Reinforcement Learning with a practical implementation of a Deep Q Network using Keras. CT5135 Research Topics in AI In-depth study of two topical research areas. CT5113: Web and Network Science This module will provide the student with the skills to extract, clean and analyse data from the Web. The focus will be graph and network analytic approaches to Web-mining. Topics include: graph theory, network modelling, social network analysis, community-finding Page 22 of 22 techniques, models of information diffusion, link prediction, evaluation techniques. There will be practical sessions on using graph-data bases and graph visualisation tools such as Gephi. The student will learn how to apply Web mining techniques to applications such as recommender systems, adaptive personalisation, authority ranking. EE551: Embedded Image Processing This module provides and introductory course in digital signal analysis covering topics such as Discrete-time systems, time-domain analysis. The z-Transform. Frequency-domain analysis, Discrete Fourier Transform (DFT). Digital filter structures and implementation. Spectral analysis with the DFT, practical considerations. Digital filter design: IIR, FIR, window methods, use of analogue prototypes. CT5121: Advanced Topics in Natural Language Processing Advanced topics in natural language processing, including deep learning for NLP, machine translation and language resources. CT5137 Knowledge Representation & Statistical Relational Learning This module introduces students to symbolic Knowledge Representation in AI. Knowledge representation and reasoning are concerned with the efficient formal representation of information and its utilization for automated problem-solving tasks. Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned topics: Foundations of knowledge representation. Propositional and first-order logic. Foundations of reasoning (deductive, inductive, abductive, probabilistic). Logic programming. SAT and Answer Set Programming. Probabilistic logics and uncertainty reasoning. Parameter and structure learning in statistical relational settings. Requirements for selecting CT5137: None