Elements of artificial intelligenceLaajuus (3 op)

Osaamistavoitteet

• Describe primary concepts related to AI
• Identify and explain relationships between AI and strongly related disciplines
• Discuss the history of AI
• Evaluate programming languages used in AI
• Solve search and planning problems with perfect information
• Formulate a real-world problem as a search problem
• Formulate a simple real-world game problem as a game tree
• Use searching to solve problems with uncertain information
• Use odds and probabilities to solve AI problems
• Solve problems using Bayes rule and Bayes naive classification

• Define machine learning
• Describe how machine learning is used to solve AI problems
• Use the nearest neighbor classifier technique to predict user behavior
• Describe characteristics of decision trees in machine learning
• Solve machine learning types of problems using linear and logistic regression
• Define neural networks
• Explain how neural networks are used to solve AI problems
• Describe how perception is used in AI problems
• Evaluate the relationship between robotics and AI
• Discuss the philosophy and the future of AI
• Discuss the philosophy and the future of AI

Sisältö

• Describe primary concepts related to AI
• Identify and explain relationships between AI and strongly related disciplines
• Discuss the history of AI
• Evaluate programming languages used in AI
• Solve search and planning problems with perfect information
• Formulate a real-world problem as a search problem
• Formulate a simple real-world game problem as a game tree
• Use searching to solve problems with uncertain information
• Use odds and probabilities to solve AI problems
• Solve problems using Bayes rule and Bayes naive classification

• Define machine learning
• Describe how machine learning is used to solve AI problems
• Use the nearest neighbor classifier technique to predict user behavior
• Describe characteristics of decision trees in machine learning
• Solve machine learning types of problems using linear and logistic regression
• Define neural networks
• Explain how neural networks are used to solve AI problems
• Describe how perception is used in AI problems
• Evaluate the relationship between robotics and AI
• Discuss the philosophy and the future of AI
• Discuss the philosophy and the future of AI

Arviointi

100% attendance is compulsory. Course grade will be derived 30% from grades on individual and team exercises, and 70% from grade on final exam. 100% attendance is required to take the final exam and to take a resit exam. A minimum score of 40% on the final exam is required to pass this course.

Grading Scale, i.e.
89 – 100% = 5 (Excellent)
77 – 88% = 4 (Very good)
65 – 76% = 3 (Good)
53 – 64% = 2 (Highly satisfactory)
40 – 52% = 1 (Satisfactory)
0 – 39% = 0 (Fail)

Edeltävä osaaminen

None