ITCS 3153: Intro Artificial Intelligence
University of North Carolina, Charlotte
Basic AI-related math fundamentals and AI concepts. Topics include: probability theory and information theory; basic search/problem-solving methods, such as uninformed search, informed search, adversarial search, local search, and constraint satisfaction problem; knowledge representation and reasoning, like propositional logic and inference; probabilistic reasoning such as Bayesian networks, sampling, and decision networks; sequential decision, such as Markov�decision processes�and�reinforcement learning; and machine learning.
Average GPA: 3.22
Grade distribution records: 3,055 students across 31 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 1,549 | 50.7% |
| B | 678 | 22.2% |
| C | 329 | 10.8% |
| D | 94 | 3.1% |
| F | 136 | 4.5% |
| W | 172 | 5.6% |
Based on 3,055 student grade records across 31 terms and 11 professors.
Instructors
- Daniel Jugan 1,482 students, Average GPA 3.55
- Julio Bahamon 541 students, Average GPA 2.76
- Sterling Mcleod 435 students, Average GPA 3.13
- Li Yang 180 students, Average GPA 3.23
- Christian Kuemmerle 98 students, Average GPA 2.06
- Waseem Shadid 88 students, Average GPA 2.94
- Minwoo Lee 74 students, Average GPA 2.64
- Richard Souvenir 54 students, Average GPA 2.33
- Samira Shaikh 54 students, Average GPA 2.97
- Wenhao Luo 31 students, Average GPA 3.06
- Shanzhen Gao 16 students, Average GPA 3.94