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All of the above will be accessed through project work and/or final examination. |
Affective (Attitudes and Values) |
N/A |
Psychomotor (Physical Skills) |
N/A |
How the Module will be Taught and what will be the Learning Experiences of the Students: |
Concepts, theory, implementations and examples presented in lectures. Term projects will enable student to study, explore and gain insight into problems, related solutions and practical issues. Weekly exercises provide a challenging and interesting means of reviewing lecture material. |
Research Findings Incorporated in to the Syllabus (If Relevant): |
N/A |
Prime Texts: |
Keshab. K. Parhi (1999) ¿VLSI digital signal processing systems¿,, John Wiley and Sons. |
Richard Conway (2007) "Course notes for CE4008", UL |
Other Texts: |
Richard. E. Blahut (1985) ¿ Fast algorithms for digital signal processing¿, Addison-Wesley Publishing company |
Hari Krishna Garg (1998) ¿Digital signal processing algorithms¿, CRC Press |
Programmes |
Semester(s) Module is Offered: |
Module Leader: |
Richard.Conway@ul.ie |
________________ |
Module Code - Title: |
CE4021 - INTRODUCTION TO SCIENTIFIC COMPUTING FOR AI |
Year Last Offered: |
2024/5 |
Hours Per Week |
Lecture |
Lab |
Tutorial |
Other |
Private |
Credits |
2 |
0 |
1 |
3 |
4 |
6 |
Grading Type: |
N |
Prerequisite Modules: |
Rationale and Purpose of the Module: |
To prepare students to take a range of Artificial Intelligence related modules by introducing the associated scientific computing, programming language and host platforms. |
Syllabus: |
1. Scripting Languages and Environments for Scientific Computing: Modern scripting languages (e.g. Python, Julia) and environments. |
2. Numeric: Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra |
3. Graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (scalar, vector fields). |
4. Acceleration: Accelerating scientific codes. Threading and parallelism. |
5. Random Numbers and Probability: Random number generation: linear congruential generators. Distributions: uniform, normal, etc. Bayesian methods: Gaussian naïve Bayes classification. |
7. Classifiers and Optimization: Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Other cost functions: cross-entropy. Application: training classifiers. Modern optimization for neural networks: Nesterov momentum, ADAM optimizer. |
8. Scientific Computing in the Cloud: Docker images. Cloud services. Running scientific code in the cloud. |
Learning Outcomes: |
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) |
1. Given a target programming language, the student will become proficient in the syntax necessary to implement standard programming constructs. |
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