杏吧论坛

Prof. Zoltan Szabo (COL.5.14)

Homepage: https://zoltansz.github.io/

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Management of Information Systems and Digital Innovation, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (杏吧论坛 and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access) and demand is typically high. This may mean that you are not able to get a place on this course. MSc in Data Science students are given priority for enrollment in this course. 

The course provides a broad overview on fundamental concepts and algorithms of artificial intelligence systems, with focus on search methods, knowledge representation, game playing, logical and probabilistic reasoning, supervised learning and reinforcement learning. We use state-of-the-art data science and artificial intelligence Python libraries and tools to translate the studied principles and methods into practice, and to gain hands-on experience in data analysis.

• Introduction: aims, history, rational actions, and agents.
• Simple uninformed search methods: graph search, tree-like search, best-first search, breadth-first search, uniform search, depth-first search, limited depth-1st search, iterative deepening search.
• Advanced informed search methods: more sophisticated heuristic search algorithms, A* search, local search, hill-climbing search, simulated annealing, local beam search, genetic algorithm, conditional plan, AND-OR search, belief states.
• Game playing: adversarial search, the minmax algorithm and its shortcomings, improving minimax using alpha-beta pruning, Type A (wide) and Type B (deep) strategies, stochastic games, EXPECTIMAX search.
• Constrained satisfaction problems (CSPs): standardising search problems to a common format, backtracking algorithm for CSPs, heuristics for improving the search for a solution, constraint propagation and consistency, solving Sudoku.
• Knowledge representation and logical reasoning: representation of common sense knowledge, inference and kno