Version  1.0 
Unit Name  Data Analytics and Artificial Intelligence 
Unit Code  BIA206 
Unit Duration  1 Term 
Award 
Bachelor of Science (Engineering) Duration 3 years 
Year Level  Two 
Unit Creator / Reviewer  Dr. Imtiaz Madni 
Common/Stream :  Stream 
Pre/Corequisites  N/A 
Credit Points 
3 Total Course Credit Points 81 (27 x 3) 
Mode of Delivery  Online or oncampus. 
Unit Workload  (Total student workload including “contact hours” = 10 hours per week) Prerecordings / Lecture – 1.5 hours Tutorial – 1.5 hours Guided labs / Group work / Assessments – 2 hours Personal Study recommended – 5 hours 
Unit Description and General Aims
This unit explores aspects of Artificial Intelligence and Data Science using computer science programming language such as Python. Machine learning and data science, as tools for artificial intelligence, are some of the most widely adopted scientific fields. The purpose of this unit is to provide the most important aspects of artificial intelligence and machine learning by presenting series of comprehensive yet simple lectures and tutorials using Python programming language. This unit includes handson guiding lectures with practical case studies of data analysis problems effectively. Students will learn data analysis, regressions, clustering, neural networking, etc. by using pandas, NumPy, IPython, and Jupiter in the Process.
Learning Outcomes
On successful completion of this Unit, students are expected to be able to:
 Analyse the equilibrium of rigid bodies.
Bloom’s Level 4  Analyse the forces in pinjointed trusses using the method of joints and the method of sections.
Bloom’s Level 4  Construct and evaluate shear force and bending moment diagrams for beams with a variety of loads and types of support.
Bloom’s Level 3  Determine and design for axial loads and direct shear and shear stress in simple beams.
Bloom’s Level 5  Determine and analyse torsional stresses and strains in circular shafts.
Bloom’s Level 4  Determine and analyse beam bending stresses and strains.
Bloom’s Level 4
Student assessment
Assessment Type  When assessed  Weighting (% of total unit marks)  Learning Outcomes Assessed 
Assessment 1 Type: Weekly Quizzes (Topic 211) Students may complete a quiz with MCQ type answers and solve some simple equations to demonstrate a good understanding of the fundamental concepts. 
Due after Topic 3  10%  All 
Assessment 2 Type: Test (Invigilated) Example Topic: Machine learning, data processing, visualization, regression. Students may provide solutions to simple problems on the listed topics. 
Due after Topic 6  25%  1, 2 
Assessment 3 Type: Report (Final Project) [If a continuation of the midterm, this should complete the report by adding sections on: methodology, implementation / evaluation, verification / validation, conclusion / challenges and recommendations / future work. If this is a new report, all headings from the midterm and the final reports must be included.] Word length: 2000 Topic examples: AI methods, applications, and algorithms. 
Due after Topic 9  25%  3, 4, 5 
Assessment 4 Type: Exam (Invigilated) Examples: Create new applications using Machine Learning, Deep Learning, and Computer Vision. Create forecasting analysis using AI tools and predict future orders. Automatically make standalone web documents and presentations using Python Bokeh. 
Final Week  40%  All 
Tutorial Attendance & Participation Description: Attendance, presentation, discussion, group work, exercises, selfassessment/reflection, case study analysis, application. 
Continuous  5%  All 
Prescribed and Recommended Readings
Textbook:
 Subasi, Abdulhamit, Elsevier 2020, Practical Machine Learning for Data Analysis Using Python, ISBN 9780128213797. Online version available at:
 Peters Morgan, AI Sciences 2016, Data Analysis from Scratch with Python, Step by Step Guide, ISBN13: 9781721942817.
Reference:
 Vlayutham, Sathiyamoorthi, IGI Global 2020, Handbook of Research on Application and Implementations of Machine Learning Techniques, ISBN 9781522599029. Online version available at:
 Alex Galea, 2018, Beginning Data Science with Python and Jupyter, Packt Publishing Ltd. ISBN 9781789532029.
 Brian Heinold, 2012, A Practical Introduction to Python Programming, Department of Mathematics and Computer Science Mount St. Mary’s University.
 Rashid Khan, Anik Das, 2018, Build Better Chatbots, A Complete Guide to Getting Started with Chatbots, Springer, ISBN13 (electronic): 9781484231111. https://doi.org/10.1007/9781484231111
Notes and Reference texts:
 Knovel library: http://app.knovel.com
 IDC notes and Reference texts as advised
 Other material advised during the lectures
Unit Content
One topic is delivered per contact week, with the exception of parttime 24week units, where one topic is delivered every two weeks.
Topic 1
Python and Data Science Basics Quick Review
 Data types, Strings, Lists, Dictionaries
 Flow control and loops
 Conditional statements, objects, classes, plots
 Python vs R
 Data Analysis vs Data Science vs Machine Learning
Topic 2
Data Processing and Visualization
 Working with Jupyter notebooks, Pandas, NumPy
 Processing data, texts, and csv files
 Feature selection from data
 Processing information from online and internal data sources
 Goal and objectives of visualization
 Visualization with Matplotlib and pycharts
Topic 3
Introduction to Machine Learning
 Linear regression
 Overfitting and underfitting
 Regularization
 Crossvalidation
 Developing a machine learning model
 Applications of Data Mining
Topic 4
Supervised Machine Learning
 Decision trees
 KNearest neighbors
 Naïve bayes
 Logistic regression
 Support vector machines
Topic 5
Unsupervised Machine Learning
 Clustering
 Principal components analysis
 Neural network overview
 Convolutional neural networks
 Autoencoders
 Recurrent neural networks
Topic 6
Search with Artificial Intelligence (AI)
 Finding the best route from origin to destination
 Agents, state, actions
 Transition model as a function
 State space by sequence of actions and directed graphs
 Goal tests and path costs
Topic 7
Knowledge and Conclusions in AI
 Knowledgebased agents and sentences
 Propositional logics and symbols
 Logic connectives, Implication, Biconditional
 Knowledgebased models
 Inference and knowledge engineering
 Modus ponens, elimination, double negation, De Morgan’s law, etc.
Topic 8
AI for Uncertainty and Probability
 Possible worlds scenario and axioms in probability
 Unconditional and conditional probability
 Random variables and independence
 Baye’s rule and joint probability
 Probability rules: Negation, inclusionexclusion, marginalization
 Bayesian networks, inference, sampling
 Markov’s models, assumptions, and chains
Topic 9
Optimization of AI Algorithms
 Local search
 Hill climbing
 Simulated annealing
 Linear programming and constraint satisfaction
 Node and arc consistencies
 Backtracking search
Topic 10
Neural Networks
 Activation functions
 Neural network structures
 Stochastic and minibatch gradient descent
 Multilayer neural networks
 Back propagation and overfitting
 Tensor flow
Topic 11
Computer Vision and Image Processing
 Computer vision and image convolution
 Convolutional neural networks
 Deep learning for computer vision
 Image classification and retrieval
 Object detection
 Semantic segmentation
Topic 12
Project and Unit Review
In the final week, students will have an opportunity to review the contents covered so far and will be engaged in practical engagement mini projects. Opportunity will be provided for a review of student work and to clarify any outstanding issues. Instructors/facilitators may choose to cover a specialized topic if applicable to that cohort.
Software/Hardware Used
Software

Software: Pytorch, KNIME, Apache Mahout, MATLAB

Version: N/A

Instructions: N/A

Additional resources or files: N/A
Hardware
 N/A