ST214: Mathematical Analysis of Experimental Data
Term: August
Credits: 3:0
For graduate students only
This course is aimed at graduate students working in data analysis. As outlined in the course content, it exposes the student to collecting and analyzing data. It also bridges the gap between physics and mathematics giving due importance to both.
Prerequisites: None
Syllabus
Design of Experiments, Data types, and data gathering tools. Errors, systematic & random errors, methods to minimize them, and account for them. Measurement variability. Instrument calibration and corrections at different scales. Significant figures. Uncertainty analysis and curve fitting; Data analysis of data distribution, normal, and t-distribution, confidence interval and hypothesis testing. Design of experiments: replication, randomization, blocking and controls. ANOVA, single factor experiments, randomized blocks, Latin square designs, factorial and fractional factorial designs. Simple and multiple linear regressions. Mathematical analysis of experimental data from problems in fluid flow, heat transfer, and combustion.
UES 314: Design Principles in Environmental Engineering
Term: August
Credits: 3:0
For undergraduate students
This is a core undergraduate course of the earth and environmental major of the bachelor of science program. As outlined in the course content, it exposes the student to acquire knowledge and understanding of the principles upon which environmental engineering is based, including general engineering, mathematical and scientific computations as well the physical, chemical, and biological science.
Prerequisites: None
Syllabus
Laws of conservation: mass, energy, and momentum balances. Fundamentals of chemical reaction engineering: thermodynamics, stoichiometric and kinetics of chemical reactions, chemical reactors – stirred tank and plug flow reactors. Design for wastewater treatment processes: physical unit operations such as sedimentation and filtration, chemical and biological treatment processes. Design for air pollution control: gas-liquid interactions, absorption and adsorption processes, particulate emission control
ST228: Data Analysis, Machine Learning, and Artificial Intelligence
Term: January
Credits: 3:0
For graduate students
This course is aimed at graduate students working in data analysis, machine learning, and artificial
intelligence. As outlined in the course content, it exposes the student to collect and analyze data. It also
bridges the gap of physics and mathematics giving due importance to both. Students will learn essential
concepts, techniques, and tools for processing, analyzing, and making predictions from data, as well as
leveraging AI technologies for problem-solving and decision-making. Through hands-on projects and real
world examples, participants will gain practical skills to apply data analysis and machine learning
techniques in various domains
Prerequisites: None
Syllabus
Introduction to data analysis and tools: Introduction to DA, importance of DA, data types, collection and storage, data cleaning and preprocessing, exploratory DA, advanced exploratory DA, and feature engineering.
Machine learning basics: Introduction to ML, supervised learning, unsupervised learning, advanced.
Deep learning and AI applications: Introduction to deep learning and DI application, ANNs, CNNs, RNNs,
Natural language processing, Computer vision, Real-world case studies
ST224: Renewable Energy
Term: August
Credits: 3:0
For postgraduate students
The PG Level Advanced course in Sustainability Engineering and Smart Cities equips individuals with essential knowledge and capabilities crucial for sustainable development, a pivotal concept shaping the world today. The programme places a strong emphasis on integrating interdisciplinary knowledge from engineering, environmental science, policy, design, and safety engineering. This is a 9-month hands-on programme, where participants delve into diverse topics like renewable energy, smart city data analytics, waste management, low-carbon infrastructure, and more. You will build expertise by leveraging an immersive learning format packed with faculty-led interactive sessions, hands-on exercises, real-world case studies, campus immersion, Assignment & Mini Projects..
Prerequisites: None
Syllabus
Introduction to Renewable Energy, Solar Energy, Wind Energy, Biomass and Bioenergy, Hydroelectric and Ocean Energy, Geothermal Energy, Energy Storage and Grid Integration, Renewable Energy Policies and Economics, Environmental and Social Impacts, Future Trends and Innovations.
CCE224: From Data to Decision: Machine Learning and AI for Real-World Science and Engineering
Term: January
Credits: 3:0
For graduate students
This course is aimed at participants interested in learning to use tools from data analysis, machine learning, and artificial intelligence for solving real world problems in science and engineering. The emphasis is on identifying and modelling problems, collecting and curating data, building models and interpreting the results, in various domains.
Prerequisites: None
Syllabus
Introduction to data driven problem solving; Data types, collection and curation; Introduction and relevance of Data Analysis (DA), exploratory DA, visualization. Foundational statistics; Introduction to machine learning; types and models of learning. Neural networks and deep learning; Modern AI systems; Real-world case studies.
CCE: From Data to Decision: Water/Wastewater Treatment or Management
Term: August
Credits: 3:0
For graduate students
This course is aimed to gain a deep understanding of the fundamental principles of water and wastewater treatment, to equip participants with the knowledge and skills necessary to design, operate, and manage effective water and wastewater treatment systems. This will include understanding the physical, chemical, and biological processes involved, as well as the regulatory frameworks governing water quality and wastewater discharge. The course aims to foster proficiency in ensuring optimal performance, efficiency, and compliance with environmental standards, while also exploring sustainable practices and technological advancements in the field.
Prerequisites: None
Syllabus
Unit I: Type and sources of water pollutants; types of wastewaters; water quality: physical, chemical and biological; general principles of sample collection and data analysis. Unit II: Design of treatment units. Unit III: Steps of water and wastewater treatment under primary & secondary and tertiary treatment; sludge handling & treatment. Unit IV: Microbiology of domestic water and wastewater. Unit V: Industrial wastewater treatment and Advanced oxidation processes. Unit VI: Standards and policy aspects for water and wastewater management.