Mechanical Engineering

The core spirit of machine learning is to learn the relevance of the data hidden in the data from the existing data through the mathematical model of the fusion hypothesis, so as to achieve the purposes of quantitative analysis, inference exploration, prediction, decision-making, etc. Machine learning can be roughly divided into two categories: machine learning hand-crafted features and deep learning. The main difference between the two is that the former is artificially designed and selected to describe the characteristics of the data, while the latter relies on deep learning theory to extract features.

This course mainly focuses on the introduction and exploration of the first type of machine learning (machine learning with hand-crafted features). The course uses actual medical imaging data to introduce typical methods of hand-crafting various features. And through actual clinical problems, implement hand-crafted features and understand their advantages and disadvantages. At the same time, in the part of machine learning algorithm, it will cover a variety of supervised, unsupervised and hybrid learning methods, such as: Linear Discrimination, decision Tree, Neural Network, Support Vector Machine, Bayesian Learning, Clustering, Reinforcement Learning and other methods.

This course teaches students the physics of heat transfer. Topics include steady conduction, transient conduction, convection, radiation, and heat exchangers. For all of these topics, practical implementation through solving small design-like problem is studied.

Design is often regarded as the central creative activity of engineering. This course enhances the skills of analysis and synthesis required to develop solutions to open-ended problems. This course teaches techniques for the effective evaluation and communication of design ideas. To support this the students also acquire a knowledge of materials and manufacturing processes selection along with the most common component/system failure modes.

This course discusses fluid statics (hydrostatic forces on submerged plane and curved surface, buoyancy, fluids in rigid motion), fluid kinematics(lagrangian and eulerian descriptions-acceleration field and material derivative, streamlines, streaklines, pathlines, profile plot, vector plot, contour plot), Reynolds transport theorem, control volume analysis, conservation of mass, conservation of momentum (Newton's Laws and choosing control volume, linear momentum and angular momentum), conservation of energy, mechanical energy and efficiency, the Bernoulli equation and its applications, general energy equation and energy analysis of steady forms, dimensional homogeneity, dimensional analysis and similarity, method of repeating variables and the Buckingham pi theorem, ideal flow, compressible flow.

This course introduces students to the practical elements of mechanical design and manufacture to prepare them for the project work. It explores the full extent of the design process from customer brief through to conceptual and detail design. The instruction extends to teaching practical manufacturing skills to enable students to make these designs themselves in the departmental workshops.

This course covers the process of engineering design, manufacturing methods, and the relationship between them. The course trains students in the methodology of all stages of engineering design: from the analysis of the client statement to the manufacturing of the design. It develops the practical, theoretical, and computational engineering skills relevant to the design process. It also develops an appreciation of sustainability in engineering design, and an understanding of how design decisions can affect the environmental and economic costs of the design and product.