Uncertainty Modelling in AI covers modelling methods for reasoning with uncertainty. This course covers probabilistic models including Bayesian networks and Markov networks. It has applications in speech, vision, natural language processing, medical informatics, bioinformatics, data mining and others.
Topics covered include Conditional Independence, Graphical Models, Inference using Graphical Models and Learning from Data etc.
Algorithms at Scale covers advanced techniques for design and analysis of algorithms and data structures, with emphasis on efficiency and scalability. This course covers a variety of algorithmic topics when copying very large data sets, including modern tools and techniques in algorithm design to handle scaling data and streaming data.
Topics covered include Sampling, Markov Chains, Sublinear-time Algorithms, Moment Estimation, Dimension Reduction, Compressive Sensing, Locality-Sensitive Hashing, Online Learning etc.
3D Computer Vision covers mathematical concepts and algorithm that recover 3D geometry of camera motions and structures in its environment.
Topics covered include Projective Geometry, Camera Model, One-/Two-/Three-/N-View Reconstructions, Stereo, Generalized Cameras and Nonrigid Structure-from-Motion etc.
Systems Security introduces fundamental notions and requirements in computer system security and mechanisms that provide security in various systems and applications. It covers security perspective in popular computer systems, such as desktop systems, mobile systems, and web-based systems.
Topics covered include Software Security, Operating System Security, Mobile Security, Web Security, Trusted Platforms, and Auditing and Forensic Analysis etc.
Distributed Systems provides basic concepts and principles of distributed operating systems, interprocess communications, distributed file systems, shared data, and the middleware approach.
Topics covered include Characteristics of Distributed Systems, Process Management Communication in Distributed Systems, Distributed Synchronization, Distributed Real-time Systems, File Systems, Naming Security, Fault Tolerant Distributed Systems, Distributed Simulation, WWW as an application of Distributed Systems etc.
AI Planning and Decision Making introduces major concepts and paradigms in planning and decision making in complex environments. It covers issues, challenges, and techniques in problem representation, goal or objective specification, response selection, and action consequence for a wide range of strategic and tactical planning and decision making situations.
Topics covered include Deterministic and Nondeterministic Planning, Practical Planning, Acting under Resource Constraints and Uncertainty, Expected Utility and Rational Decision Making, Decision Networks, Markov Decision Processes, Elementary Game Theory, and Multi-agent Planning and Decision Making etc.
Database Applications Design and Tuning addresses the design and performance tuning of database applications. This course focuses on relational database applications implemented with relational database management systems.
Topics covered include Normalization Theory (Functional, Multi-valued, Join Dependency, Normal Forms, Decomposition and Synthesis methods), Entity Relationship, SQL Tuning (Performance Evaluation, Execution Plan Verification, Indexing, De-normalization, Code Level and Transactions Tuning), Design and Performance Tuning of Non-Relational Database Applications (Network and Hierarchical Models and Nested Relational Model, XML, NoSQL systems) etc.
Intelligent Robots: Algorithms and Systems introduces core algorithms and system architectures of intelligent robots. It covers main system components for sensing, decision making, and motion control and their integration for core robot capabilities, such as navigation and manipulation. It has applications in self-driving cars, aerial drones, and object manipulation.
Topics covered include key algorithms for robot intelligence through Inference, Planning, and Learning, practical experiences with modern robot systems etc.
Formal Specification and Design Techniques provide a precise and unambiguous description of a computer system, to verify important properties and detect design error before system development begins. This course covers various formal specification and design techniques for modelling object-oriented systems, real-time distributed systems, and concurrent reactive systems.
Topics covered include State-based Notations (Z/Object-Z), Event-based Notations (CSP/Timed-CSP), Graphical Modelling Notations (StateChart, UML) etc.
Computer System Performance Analysis provides working knowledge of computer performance evaluation and capacity planning. This course covers ways to identify performance bottlenecks, predict when performance limits of a system will be exceeded, and characterize present and future workload to perform capacity planning activities.
Topics covered include Performance Analysis Overview, Measurement Techniques and Tools including Workload Characterization, Instrumentation, Benchmarking, Analytical Modelling Techniques (Operational Analysis, Stochastic Queueing Network Analysis), Performance of Client-Server Architectures, and Capacity Planning case studies etc.
Advanced Maths I provides firm foundations of single variable calculus. This course covers the basic concepts, techniques, and applications of two branches of calculus - differentiation and integration.
Topics covered include Computational of Derivatives, Trigonometric Functions, Mean Value Theorem, Newtons Method, Fundamental Theorem of Calculus, Techniques of Integration etc.
Advanced Maths II is divided into Linear Algebra and Vector Calculus. Linear Algebra introduces methods of finding solution vector x in the equation Ax = b. Vector Calculus deals with single variable and multivariable functions.
Topics covered include Fundamental Theorem of Linear Algebra, Eigenvalues and Eigenvectors of a Matrix, Partial Derivatives and Linear Approximations to Multivariable functions, Level Curves, Vector Fields etc.
Chemistry and Biology: Natural World presents the complementary nature of chemistry and biology by providing a solid understanding of the fundamentals of general chemistry, biochemistry, and molecular biology. This course emphasises on scientific methodologies, experimental design, problem-solving, and data analysis.
Topics covered include concepts of Atomic Structure, Thermodynamics, Acid-Base Reactions, Reaction Kinetics and Catalysis.
Introduction to Design presents concepts of design at a variety of scales and through both engineering and architectural design disciplines. This course covers design processes, modes of thinking and analysis, relationships between form, space, structure and materiality, and social and cultural aspects of design.
Physics I is a first year engineering mechanics course, based on corresponding Physics I course offered at MIT.
Topics covered include One Dimensional Motion, Two Dimensional Motion, Force and Newton's Law of Motion, Energy, Systems of Particles, Center of Mass, Momentum conservation, and Two Dimensional Rotational Motion Kinematics and Dynamics.
Physics II is a first year engineering electromagnetism course, based on similar course offered at MIT.
Topics covered include Electrostatics (Coulomb's Law, Gauss' Law), Electric Structure of Matter, Electric Current (Ohm's Law, DC Circuit Theory with capacitors), Magnetostatics (Lorentz Force Law, Biot-Savart Law, Ampere's Law), Time-Varying Fields (Faraday's Law of Induction), Maxwell's Equations (Poynting vector), Electromagnetic Waves.
World Civilisations and Texts hones critical reading, writing, and thinking skills by engaging with the interpretative richness of these major documents of human civilisation. This course helps identify the connections between various texts and the ways that conversations develop between thinkers throughout history, and encourages developing own interpretations by being exposed to different ways of reading and interpreting classic texts.
Topics covered include Gender and Sexuality, Ecological Implications, Ethics, Historio-political lens and Literature and Narrative.
Theorising Society, The Self, And Culture is a social science course that provide the conceptual tools necessary to make sense of the complex problems faced in the modern world.
Topics covered include What is society and what are the social sciences, How is society organized and how does it change, How and why do people act and How do people express group values and norms.
Engineering in the Physical World covers necessary framework to understand, model and engineer the physical world at micro, macro or lumped scale.
Topics covered include Molecular Origin of Thermal and Mechanical Phenomena, Chemical and Electrical Domains, Statistical Mechanics to Thermodynamics and Macroscopic Behavior; Conservations Law for Energy, Mass, Momentum, Origins and Limits of Macroscopic Constitutive Relations, Physical Systems (Lumped Approximations, Equilibrium Networks) and Applications to Energy and Materials.
Engineering Systems Design introduces various engineering systems and challenges associated with their design, planning and control. This course covers important industrial applications where system thinking and modelling improve system performance. This includes identifying, evaluating and modelling the objects of an engineering system, the stakeholders, the key performance criteria, key trade-offs, critical constraints, uncertainties and risks.
Topics covered include Modelling, Analysis and working with real data and leverage tools and methods from data analysis, probability and optimization.
Modelling the Systems World is divided into Systems Modelling and Systems Optimisation. Systems Modelling introduces the basics of mathematical modelling. Systems Optimisation covers mathematical tool for optimisation.
Topics covered include Differential Equations, Laplace Transform, Optimisation, Numerical Solution Algorithms and Networks.
Optimization covers a broad range of optimization algorithms and models.
Topics covered include Linear Programming, Simplex Algorithm, Duality, Sensitivity Analysis, Two Player Zero-Sum Games, Network Optimization, Minimum Cost Flow, Network Simplex Algorithm, Integer Programming, Branch and Bound Methods, Cutting Plane Methods and Dynamic Programming.
The Digital World covers the principles of modularity and abstraction, two fundamental ideas in many areas of design.
Topics covered include Object-Oriented Programming (OOP) and how to model state machines using abstraction provided in OOP in Python programming language.
Probability covers the basic tools for modelling and analyzing situations that involve uncertainty and will consider applications in various fields.
Topics covered include Axioms of Probability, Conditional Probability and Independence, Random Variables (Discrete, Continous, Jointly Distributed), Random Vectors, Probability Distribution, Properties of Expectation and Limit Theorems.
Biology focuses on the fundamental principles of cellular biology and genetics. The course focus on the intrinsic and extrinsic factors that influence the normal functioning of biological systems at different levels or organization and how current technology has driven scientific investigation and advancement of our understanding of biological systems.
Topics covered include Cell Signaling, Cell Cycle, Cell Cycle Regulation, Sexual Reproduction, Genetic Analytics in Drosophila, Human Genetics, and Cancer.
Introduction to Physical Chemistry presents a solid foundation of the fundamentals of physical chemistry from the atomic level to the molecular level.
Topics covered include Semiconductors, Quantum Dots, Spectroscopy, Transition Metals and Electrochemistry.
Organizational Processes is organized around three different perspectives on organizations: the strategic design perspective, the political perspective, and the cultural perspective. Each perspective offers a different approach on what an organization does, and correspondingly offers different 'tools for action'. The course probe some of the psychological and social processes that inherent in organizations and examine the implications of these insights for understanding the prospects and challenges of working in organizations.
Operations Management is an introduction to methods for managing manufacturing and service operations. Special emphasis will be given to the link between operational issues and strategic objectives regarding cost, responsiveness, flexibility, product variety, and customer differentiation. This course also discuss the implications of various emerging technologies, business practices and government regulations.
Topics covered include Production Planning and Scheduling, Inventory Control, Supply Chain Coordination, Service System Design and Queuing Theory.
Decision Analysis introduces decision analysis tools and methods with a focus on decision making under uncertainty and the structuring of hard decision problems in engineering, business and public policy contexts. The value of flexibility in engineering systems is also appreciated through building models.
Topics covered include discounted cash flow analysis, heuristics and biases, structuring and solving decision trees, applying Bayes' Theorem to update prior distributions upon observing new information and calculate the value of information, expected utility theory and modelling using Monte Carlo simulation.
Quality & Reliability introduces modern methods and strategies (i.e. statistical quality control, design of experiments, six sigma) to analyze, manage and improve engineering quality and reliability.
Topics covered include key concepts, principles and methods for quality design and their applications to various areas in practice.
Statistics provides an introduction to applied statistics and data analysis. Applications and examples based on engineering systems data will be given.
Topics covered include Collecting and Exploring Data, Estimation and Hypothesis Testing, Linear Regression, Analysis of Variance and Elementary Methods in Non-Parametric Statistics.
Simulation introduces the basic ideas of simulation and its use in the analysis of various systems.
Topics covered include use and examples of simulation, general principles, statistical models, random number generators, analysis of simulation data and applications.
Project Management covers theory, models, methods and skills needed for project management, introducing leadership, communication and teamwork skills as relevant for successful projects.
Topics covered include network models for conceptualizing projects, planning and scheduling methods for project management, methods for addressing project dynamics due to task uncertainty and project risk, monitoring and rescheduling of projects, project accounting and organizational considerations in managing people and projects.
Introduction to Stochastic Modelling deals with the use of probability to model such situations in which uncertainty is present. This course aims at providing basic tools in the analysis of stochastic models and processes.
Topics covered include Discrete Time Markov Chains, Poisson Processes, Continuous-time Markov Chains and Queueing Systems.
Network Modelling demonstrates how the analysis of network models and algorithms are applied to designing, planning and operating various types of engineering systems.
Topics covered include Fundamentals of Graph Theory, Network Structures and Properties, Network Dynamics and Evolution, Network Performance and Information Flows and will illustrate these concepts through variety of technical and sociological examples.
Investment Science focus on the financial theory of asset pricing. While it is broad in scope and covers many different topics relating to finance, particular emphasis will be placed on topics related to financial markets.
Topics covered include present value, asset valuation, expected utility, portfolio selection and the trade-off between risk and return.
Rice Cultures Technology, Society, and Environment in Asia covers the social scientific study of human-environment interactions through the history and culture of a single agricultural crop: rice. The long history of the cultivation of rice forms the core of an independent origin of civilization, distinct from other Eurasians sites that were all based around wheat/barley and millet. Some scholars hypothesize that the cultivation of rice supports a distinctive Asian form of society and mode of historical or technological development, that differs from standard frameworks of 'Western' modernization. This course explores how the cultivation of rice raises exciting and provocative questions about the relationships among society, technology and environment and asks whether Asia's history and culture of rice and provide an alternative figure of technological development and design, and perhaps a model for a sustainable relationship between human and their natural environments.
Microeconomics seeks to understand economic behavior by starting with individual economic agents - consumer and firms - and move from a bottom-up approach to understand market demand and supply.
Topics covered include analyzing market in equilibrium, advantages and disadvantages of various market structures and circumstances under which the market runs into difficulties for delivering efficient outcomes.
Capstone project brings together students from different courses to work in design teams, contributing respective expertise and skills to solve real-world challenges. It is a realistic design situation where projects usually span multiple discipines and require team-based efforts to create a solution.
The project is elaborated below.
Capstone project brings together students from different courses to work in design teams, contributing respective expertise and skills to solve real-world challenges. It is a realistic design situation where projects usually span multiple discipines and require team-based efforts to create a solution.
The project is elaborated below.
Derivative Pricing and Risk Management introduces the theory and practice of mathematical finance.
Topics covered include basics of derivative securities; interest rate and bonds; forward and futures contracts, hedging using futures contracts; option contracts and arbitrage relationship; binomial model, no-arbitrage pricing, risk-neutral pricing, and American options pricing; Brownian motion, Black-Scholes-Merton model, delta hedging, Greek letters, implied volatility, and volatility smile; classical and advanced portfolio management and execution models and strategies.
Financial Systems Design is designed for students who are interested in careers in the securities, banking, and financial management and consulting industries, or as quantitative analysts in corporate treasury and finance departments of general manufacturing and service firms.
Topics covered include portfolio theory, derivatives valuation, and financial risk analysis, completing core courses in stochastic processes, optimization, simulation and statistics.
Machine Learning is the study of algorithms that improve their performance at some task with experience. This course will give an in-depth understanding of a broad range of machine learning algorithms from the basic to state-of-the-art, such as: naïve Bayes, logistic regression, neural networks, clustering, probabilistic graphical models, reinforcement learning and SVMs.
Topics covered include perception, linear and logistic regression, support vector machines, kernel methods, neural networks and deep learning, clustering, generative models, mixture models and expectation maximization, hidden Markov model, Bayesian networks and reinforcement learning.
The Analytics Edge uses data and analytics to give an edge in improving various aspects of life. The statistical software R and spreadsheets will be used in the course.
Topics covered include real world examples of how analytics is being used with examples from Moneyball, Watson and the Framingham Heart Study among others, tools of analytics such as linear and logistic regression, classification and regression trees, graphical models, visualization, text analytics, clustering and optimization in practice.
Global Shakespeares respond to cinematic productions that restore Shakespeare's characters and themes to popular culture - both in his own time and around the world. This course will read and analyze Shakespeare's original playtexts alongside global cinematic productions of his plays. This course will analyze how these films respond to living issues of politics, gender, race and nationhood.
The Word and the World introduces literary theory and provides a basic understanding of how to approach and interpret texts as a means of analysing them. The course introduces important strategies for engaging with literary texts developed in the twentieth century and beyond, paying special attention to poststructuralist theories, their precursors and legacy.
Topics covered include Structuralism and Myths, Reading and Deconstruction, Identity Politics, Foucault and the Frankfurt School and Comparativism.
Advanced Topics in Optimisation focuses on modelling, formulations and optimisation techniques. This course introduces the basics and design of algebraic modelling languages and spreadsheet models for describing optimisation problems. The application techniques will be applied to proving optimality of schedules, integer programming formulations, heuristics, deterministic and stochastic dynamic optimisation frameworks.
Topics covered include single machine scheduling, parallel machine environment, flow shop scheduling, job shop scheduling and uncertainties in scheduling
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