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50 - LONG - Long-Term Programs
LONG 160 - BigBang of Science: Complete Guide (15 Days)
Course Description
First section: modelling optimisation problems and selecting strategic decision-making criteria. The lectures are built to solve hard combinatorial optimisation problems by first modelling them and then using an off-the-shelf methods to solve them. Combinatorial (satisfaction or optimisation) problems the main characteristics of various constraint solving techniques, heuristics and good practice in modelling and solving combinatorial problems, the section contains a number of LAB hands-on practical problems. More importantly, you learn the characteristics of choosing right features for decision making process
Section Two: A practical grounding in the world of artificial intelligence (AI) and its business applications, equipping you with the knowledge and confidence you need to transform your solution into an innovative, efficient, and sustainable one. In addition, it enhances the ability to lead informed, strategic decision-making performance by integrating key AI problem solving paradigm into your solution.
Section Three: build the essential skills of modeling, abstraction, analysis, simulation, and validation, all of which are central in engineering, natural sciences, social sciences, medicine, and many other fields. Many scientists learn these skills implicitly, but in most real life applications they are not able to experience that explicitly, and as a result many real life applications has not been done. That’s the problem this SECTION is meant to address. The goal is to teach the entire modeling process and give trainees a chance to design their own simulation room.
Course Objectives
- Modelling optimisation problems
- Selecting strategic decision-making criteria
- Learn the characteristics of choosing right features for decision making process
- Transform your solution into an innovative, efficient, and sustainable one
Who Should Attend?
- IT technicians
- IT engineers
- IT technical support teams
- IT technical services specialists
- Anyone who would like to know more about the field
Course Details/Schedule
Week 1
- Shortest paths and trees
- Polytopes, polyhedra,
- bipartite graphs, matching & covers
- Menger’s theorem
- View on nondeterministic polynomial time NP
- Problem of Edge-colourings of bipartite graphs
- Deep into linear programming
- Multicommodity flows
- Matroids Problem
Week 2
- Structure of Data & Employment of Data
- Philosophy of Math + Structure of Equation &
- Functions
- Math Hacks of AI
- Philosophy of AI
- World of GRADIENTS AND OPTIMIZATION
- Regression Problem
- Classification Problem
- Clustering Problem
- Deep & Transfer Learning Problem
Week 3
- Introduction to simulation modelling + randomness and random numbers
- Probability and data generation process
- Exploring Monte Carlo simulation & Markov decision process + Resampling methods
- Using simulation to improve & optimize systems
- Simulating physical phenomena using Neural network
- Using simulation for project management
- Using simulative modelling for financial engineering
