About Programme
AI for Managers is a 8-month long programme comprising 8 online modular courses and 2 optional courses stacked together based on the order of their sequence in a learning curve. It aims to make the knowledge of Artificial Intelligence and its components such as Statistical Learning, Machine Learning, and Deep Learning accessible to a large number of interested candidates from fresh graduates to senior managers who aspire to become competent Decision Makers.
Important Dates
Last Date of Registration: 10th January 2025
Programme Commencement: 20th January 2025
Program Objective
At the end of the programme the participants will be able to:
Recognize the emergence of Artificial Intelligence and Machine Learning as a competitive strategy.
Understand foundations of data science on which the AI models are built.
Learn descriptive, diagnostic, predictive, and prescriptive analytics and their applications in generating solutions for business problems.
Understand and apply machine learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning algorithms to solve problems across various functional areas of management.
Learn the functioning of artificial neural networks (ANN) and deep learning algorithms such as recurrent neural network (RNN) and Convolutional Neural Network (CNN) with applications.
Demonstrate data visualization and storytelling through data.
Demonstrate hands-on experience with software such as Python, Microsoft Excel, R, SPSS and Tableau.
Apply AI techniques to solve problems in various sectors such as Aerospace, Banking financial services and insurance (BFSI), E-commerce, Manufacturing, Retail, Sports and Services.
| Batch | Base Fee | 18% GST | Total |
|---|---|---|---|
| Batch 3 | 2,25,000 | 40,500 | 2,65,500 |
Course Curriculum
Programme Duration: 8 Months
Course Length: 8 Courses
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Foundations of Data Science
No of Modules: 5
Instructor: Professor U Dinesh Kumar
What You’ll Learn?
This course focuses on equipping managers with essential statistical techniques for data-driven decision-making, covering both theoretical and practical aspects. It introduces data science fundamentals, including data summarization, visualization, and measures of central tendency. Participants will learn probability concepts such as joint and conditional probability, Bayes’ theorem, and association rule mining. The course also covers probability distributions (binomial, Poisson, normal), sampling methods, and the central limit theorem. Through hypothesis testing and estimation techniques, learners will develop the skills to analyze, interpret, and communicate data effectively, applying statistical methods to facilitate business decisions.
Data Visualization and Story Telling
No of Modules: 2
Instructor: Sharada Sringeswara
What You’ll Learn?
This course covers topics on data preparation, preprocessing, and data visualization. Learners will focus on data quality checks, cleaning, imputation techniques, and using algorithms like K-Nearest Neighbors (KNN) for data imputation. They will also explore principles of data visualization design, data encoding methods, and learn to use Tableau for creating business dashboards. Additionally, the course emphasizes storytelling through data, helping participants effectively communicate insights using visual components.
Predictive Analysis
No of Modules: 4
Instructor: U Dinesh Kumar
What You’ll Learn?
This course covers key concepts of predictive analytics, including regression model building frameworks, data pre-processing, diagnostics, and validation. You’ll explore simple linear regression (coefficient of determination, residual analysis), multiple linear regression (regression coefficients, heteroscedasticity, and multi-collinearity), and logistic regression (binary and multinomial), focusing on probability estimation, error matrices, and ROC curves. The content also covers forecasting using ARIMA models and highlights practical applications of predictive analytics in industries like retail, healthcare, and financial services. Additionally, you’ll gain skills in using software tools like MS Excel, R, Python, and SPSS
Machine Learning with Business Applications
No of Modules: 3
Instructor: U Dinesh Kumar
What You’ll Learn?
This Course covers key machine learning concepts and techniques, including decision tree learning, classification and regression trees (CART), and Gini impurity index. They will also explore Chi-Square Automatic Interaction Detection (CHAID) for feature selection, focusing on solving classification problems with imbalanced data. The course covers ensemble methods such as Random Forest, Bootstrap Aggregating (Bagging), and understanding variable importance. Boosting techniques like Adaptive Boosting and Gradient Boosting will also be introduced. The course equips participants with practical skills to apply machine learning algorithms to real-world business applications.
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Reinforcement Learning
No of Modules: 2
Instructor: U Dinesh Kumar
What You’ll Learn?
This course covers reinforcement learning (RL) algorithms, focusing on decision-making under uncertainty to maximize rewards. It introduces stochastic processes, Markov models, and applications of these models in business, finance, retail, and operations. Participants will learn about steady-state probability estimation, brand switching, loyalty modelling, market share estimation, and Google’s ranking algorithm. The course also covers Poisson processes and their applications in marketing, operations, and insurance, along with retail promotions and warranty analytics. Additionally, dynamic programming, Markov decision processes, policy iteration, and value iteration algorithms are explored, providing a solid foundation in RL for complex problem-solving in uncertain environments
Artificial Neural Network and Deep Learning
No of Modules: 8
Instructor: Naveen Kumar Bhansali
What You’ll Learn?
This course provides an in-depth exploration of Artificial Neural Networks (ANNs), deep learning, and Generative AI, using TensorFlow/Keras. Participants will learn the distinction between machine learning and deep learning, focusing on representational learning and its real-world applications. The course covers topics such as building and training neural networks, understanding CNN architectures for computer vision, and NLP models using RNNs, LSTMs, GRUs, and Transformers. Additionally, participants will delve into hyper-parameter tuning, regularization, and advanced techniques like YOLO for object detection. The course also explores unsupervised learning with autoencoders, transfer learning, and building recommender systems, providing comprehensive hands-on training with modern AI architectures.
Generative AI
No of Modules: 4
Instructor: Naveen Kumar Bhansali
What You’ll Learn?
Generative AI is revolutionizing industries by addressing the challenges of unstructured data and content creation across sectors like IT, finance, healthcare, and manufacturing. It powers technologies such as language models and creative content generation, transforming media, entertainment, and marketing. This course covers three core areas: fundamental concepts of Generative AI, its applications, and deep learning techniques. Participants will explore transformers, prompt engineering, fine-tuning, and reinforcement learning with human feedback. Topics include text and image generation, retrieval-augmented generation (RAG), GANs, diffusion models, and LangChain. The course emphasizes ethical AI, deployment strategies, and real-world case studies demonstrating Generative AI’s business impact.
Machine Learning Using Python
No of Modules: 5
Instructor: Manaranjan Pradhan
What You’ll Learn?
This course provides a comprehensive foundation in machine learning using Python, starting with core Python programming and progressing to advanced machine learning techniques. Participants will learn how to explore and prepare datasets, perform basic statistics and hypothesis testing, and build regression and classification models through supervised learning. The course covers the entire lifecycle of machine learning models, from data preprocessing to model deployment, utilizing algorithms such as decision trees, logistic regression, KNN, and Random Forest. Additionally, participants will explore unsupervised learning techniques like clustering and K-Means and build recommender systems using association rules and collaborative filtering.
Who Can Apply?
Aspirants with 3+ year’s experience willing to build a career in AI/ML or wishes to enhance their skills for better career opportunities.
Entrepreneurs and business owners who want to leverage AI and ML to accelerate their business growth.
Admission Criteria
The Programme follows a comprehensive admission procedure to ensure that the learners’ goals are oriented towards a challenging experience. Participants are carefully chosen through Profile Screening and Interview
Program Highlights
Industry-relevant Curriculum
Experienced Instructors from Industry & Academia
Real-World Case Studies published by faculty at Harvard Business Publishing
Education from India’s Best Management School
Hands-on Learning Experience
Certificate from IIMB
State-of-the-art Pedagogical Tools
Peer2Peer Learning Forum
Boot Camp
Class labs/ Home Assignment
Project Presentations
Affiliate Alumni
Evaluation, Grading policy and Certification
Meet the Instructors
U Dinesh Kumar
Decision Sciences and Chair of Excellence
Kumar Rahul
Alumnus of IIMB and Research Scholar of IIMK
Manaranjan Pradhan
Alumnus of IIMB and Authored a Book on Python
Naveen Kumar Bhansali
Adjunct faculty and alumnus of IIM Bangalore
Sharada Sringeswara
IIM Bangalore and BITS Pilani alumni
Testimonials
Syamkumar Rajasekharan Nair
Chandrashekhar Mane PMP
Rohit Chourey
Ankita Umesh Panchariya
