This lecture will introduce probability models and analysis and reasoning methods to handle phenomena involving uncertainty appropriately. First, mathematical formulations of probability distributions will be presented based on the calculation methods of probability learned in high school. Next, we will discuss what kind of probability models can be used to describe uncertain phenomena found in nature and society. Furthermore, probabilistic reasoning, which uses probability to make inferences from occurring phenomena, will also be explained.
In problems such as business analysis and decision-making, it is necessary to handle uncertain phenomena appropriately. This lecture aims to acquire the basic knowledge to analyze and make inferences using probability theory for such problems.
By taking this course, students will be able to acquire the following skills.
(1) Basic knowledge of probability, probability distributions, and probabilistic reasoning.
(2) To be able to utilize probabilistic analysis and reasoning to solve engineering problems.
(3) To be able to apply probabilistic views and ideas to real-world problems.
random variables, probability distribution, conditional probability, binomial distribution, stochastic process, probabilistic reasoning, Naïve Bayes, Bayesian networks
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed
Course schedule | Required learning | |
---|---|---|
Class 1 | Probability (1) | Understand sets and numbers, permutations, and combinations |
Class 2 | Probability (2) | Understand axiomatic probabilities |
Class 3 | Probability (3) | Understand Conditional Probability |
Class 4 | Probability distributions (1) | Understand random variables and probability distributions |
Class 5 | Probability distributions (2) | Understand various probability distributions |
Class 6 | Multivariate data | Understand probabilistic analysis for multivariate data |
Class 7 | Stochastic process | Understand stochastic process |
Class 8 | Representation of events and probability | Understand representation method of events and probability in AI |
Class 9 | Probabilistic Reasoning (1) | Understand basic idea of probabilistic reasoning |
Class 10 | Probabilistic Reasoning (2) | Understand Naïve Bays |
Class 11 | Probabilistic Reasoning (3) | Understand Bayesian Networks |
Class 12 | Probabilistic Reasoning over Time (1) | Understand basic idea of probabilistic reasoning over time |
Class 13 | Probabilistic Reasoning over Time (2) | Understand several methods of probabilistic reasoning over time |
Class 14 | Conclusion | Understand how to apply probabilistic models to engineering problems |
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterward (including assignments) for each class.
Nobuaki Obata: Probability and Statistics for Data Science, Kyoritsu Shuppan (in Japanese)
Stuart Russell, Peter Norvig: Artificial Intelligence: A Modern Approach, Pearson
Kazunori Matsumoto, Tetsuhiro Miyahara, Yasuo Nagai, Ryutaro Ichise: Artificial Intelligence, Ohm Sha (in Japanese)
Provide handouts when needed.
Exercise problems and Final exam.
Nothing in particular.