Pre-requisites and Outcomes
Course Name: Short term AI course for Engineering Students, 2025. Institute name: MBM University, Jodhpur
Pre-requisites
Mathematics: Linear Algebra, Calculus, Probability and Statistics; Introduction to Computer Science, basic Programming skills, arrays; Algorithm: sorting, searching, recursion, Python: A primary language used in AI development, including libraries like NumPy, pandas, TensorFlow, and PyTorch. Discrete Mathematics (optional but useful): Trees and graphs. Discrete mathematics provides a foundation for understanding algorithms, logic, and graph theory, which are often involved in AI.
Basic Concepts
What is AI? Definition; History and Evolution; Significance of AI; AI in Engineering: Civil, Electrical, Mechanical, Computer Science. Some Questions about AI, Turing Test, Goal and root of AI, Common techniques used in AI, Sub fields of AI. Knowledge representation, Physical Symbol System hypothesis (PSSH). Propositional Logic, Predicate Logic, operators for these logic.
Reasoning Patterns, State space Search
Knowledge representation in predicate logic, similarity between predicate logic and Prolog language. Editing and running Prolog program. Rules and facts. Querying the knowledge base. State space representation, traversing (searching) the state space through breadth first search (BFS), and depth first search (DFS), A* search. Robotic search and Electric grid’s Load Distribution problems and python programs.
Machine Learning
Supervised Learning, unsupervised learning, formal definition, Examples. Reinforcement Learning. Problems, algorithms, and programs. Neural Networks, layers NNs, How the learning takes place in a NN?
Natural Language Processing
NLP Defn, text data in NLP, NLP work flow, tokenization, parts-of-speech (POS) tagging, named entity recognition, stop words, pre-processing, classification, NLP tools, NLTK, NLP applications
Unsupervised learning, Social and ethical implications of AI
Clustering, applications, unsupervised learning, squared error method, K-Means Clustering. Social and ethical implications of AI