Artificial Intelligence and
Machine Learning
Instructor
Pradipta
Biswas, PhD (Cantab)
Intelligent
Inclusive Interaction Design Lab
Assistant
Professor
Email: pradipta AT iisc
DOT ac DOT in
Prerequisite·
Basic knowledge of Mathematics ·
Basic knowledge of probability and statistics ·
C++ and preferably C# or VB.Net programming skill |
What you get·
Introduction to different AI algorithms ·
Introduction to Machine Learning ·
Case Studies ·
Qualitative and Quantitative Data Analysis |
·
Lecture 1: Introduction to Artificial
Intelligence, Case Studies from Fashion Technology [Lecture notes] [Case Studies]
·
Lecture 2: Heuristic Search Algorithms,
Complexity Analysis, State Space Modelling, Application on developing
Intelligent User Interface [Lecture notes]
·
Lecture 3: Uncertainty Modelling, Conditional
Probability, Bayes’ Rule, Certainty Factor, Expert System [Lecture notes] [Expert System Example]
·
Lecture 4: Bayesian Inferencing, Bayesian
Network, Naïve Bayes Classifier [Lecture notes] [Case Study]
· Lecture 5: Introduction to Machine Learning, Classification and
Clustering, Backpropagation Neural Network, Cluster Validation Index, Case
study on predicting pointing target [Lecture Notes] [Neural Network Example]
·
Lecture 6: Quantitative Data Analysis,
Statistical Hypothesis Testing, Parametric and Non Parametric Tests, t-test,
ANOVA, Chi-Square tests, APA Reporting Style [Lecture Notes] [Sample
dataset and analysis]
·
Lecture 7: Case studies on image processing and
computer vision [Lecture I] [Lecture II]
·
Workshop on CLIPS Expert System, ML
toolboxes and eye gaze tracking sensors
·
Lecture 8: Qualitative Data Analysis, Different
Techniques to collect Qualitative Data, Introduction to Analysing Free Text
Data [Lecture Notes]
·
Lecture 9: Sequential
Decision Process, MDP, Case Study on developing a cognitive model [Lecture
Notes]
·
Lecture 10: Partial Order Planning [Lecture
Notes]
References
Russell S and Norvig
P., A Modern Approach to Artificial Intelligence
Field A., Statistics with SPSS