Loops EECS2030 B: Advanced Object Oriented Programming Fall 2019 CHEN-WEI WANG Learning Outcomes Understand about Loops : ● Motivation: Repetition of similar actions ● Two common loops: for and while ● Primitive vs. Compound Statements ● Nesting loops within if statements ● Nesting if statements within loops ● Common Errors and Pitfalls 2 of 70 […]
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Scanner: Lexical Analysis Readings: EAC2 Chapter 2 EECS4302 M: Compilers and Interpreters Winter 2020 CHEN-WEI WANG Scanner in Context ○ Recall: Lexical Analysis Source Program (seq. of characters) Scanner Syntactic Analysis seq. of tokens Parser Semantic Analysis AST1 … ASTn pretty printed Target Program ○ Treats the input programas as a a sequence of characters
constructions that transform an re into an fa that is suitable for direct imple- mentation and an algorithm that derives an re for the language accepted by anns. Tostic fa 2.4.1. Next, Scanner: Lexical Analysis Readings: EAC2 Chapter 2 EECS4302 M: Compilers and Interpreters Winter 2020 CHEN-WEI WANG fa. Figure 2.3 shows the relationship between
Loops EECS1021: Object Oriented Programming: from Sensors to Actuators Winter 2019 CHEN-WEI WANG Motivation of Loops ● We may want to repeat the similar action(s) for a (bounded) number of times. e.g., Print the “Hello World” message for 100 times e.g., To find out the maximum value in a list of numbers ● We may
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Parser: Syntactic Analysis Readings: EAC2 Chapter 3 EECS4302 M: Compilers and Interpreters Winter 2020 CHEN-WEI WANG Parser in Context ○ Recall: Lexical Analysis Source Program (seq. of characters) Scanner Syntactic Analysis seq. of tokens Parser Semantic Analysis AST1 … ASTn pretty printed Target Program ○ Treats the input programas as a a sequence of classified
CMPUT 366 F20: Representational Dimensions James Wright & Vadim Bulitko September 3, 2020 CMPUT 366 F20: Representational Dimensions 1 Lecture Outline Lecture recordings Tutorials Representational dimensions PM Chapter 1 CMPUT 366 F20: Representational Dimensions 2 Lecture Recordings About ¨% of the students are attending from outside of Canada, from substantially different time zones To include
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CMPUT 366 F20: Uninformed Search James Wright & Vadim Bulitko September 10, 2020 CMPUT 366 F20: Uninformed Search 1 Lecture Outline Tutorial next Monday In-lecture questions Uninformed search PM 3.5 CMPUT 366 F20: Uninformed Search 2 Summary of The Last Lecture Many AI tasks can be represented as search problems A single generic graph search
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CMPUT 366 F20: Probability Theory James Wright & Vadim Bulitko October 15, 2020 CMPUT 366 F20: Probability Theory 1 Lecture Outline Probability Theory PM 8.1-8.2 CMPUT 366 F20: Probability Theory 2 Uncertainty In both search and RL we assumed that the agent knows its current state s That is an abstraction/simplification in real life agents
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CMPUT 366 F20: Search James Wright & Vadim Bulitko September 8, 2020 CMPUT 366 F20: Search 1 Lecture Outline Basic search PM 3.1-3.4 CMPUT 366 F20: Search 2 Recap: Dimensions Dimension Static vs. sequential action Goals vs. complex preferences Episodic vs. continuing State representation scheme Perfect vs. bounded rationality Uncertainty: states/dynamics Interaction: offline vs online
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CMPUT 366 F20: More on RNN & Learning Outcomes Vadim Bulitko & James Wright December 1, 2020 CMPUT 366 F20: More on RNN & Learning Outcomes 1 Lecture Outline More on RNNs PM 7.1-7.2 GBC 10 Final exam details Learning outcomes CMPUT 366 F20: More on RNN & Learning Outcomes 2 RNN: Overview CMPUT 366