Tamal Ghosh, PhD
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Kolkata...
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Dr. Tamal Ghosh - Teaching
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About Qualifications Teaching Research Publications Projects Miscellaneous
Dr. Tamal Ghosh

Dr. Tamal Ghosh

Associate Professor  |  Educator  |  Researcher  |  NAAC Consultant
Dept. of Computer Science & Engineering, School of Engineering & Technology, Adamas University, Kolkata
PostDoc NTNU, Gjøvik, Norway PhD Jadavpur University B.Tech CSE NIT Calicut M.Tech IE&M WBUT
Email (official): tamal.ghosh1@adamasuniversity.ac.in
LinkedIn Download CV ORCID Google Scholar Scopus Web of Science
Sections
About Qualifications > Teaching Research Publications Projects Miscellaneous
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LinkedIn Institute Page Vidwan-438955

Courses Taught

CSE 11112
Introduction to Artificial Intelligence (B.Tech) - Summer 2024, 2025, 2026 Slides
CSE 21816
Machine Learning - Winter 2023, Summer/Winter 2024, Summer/Winter 2025 (PhD) Slides
CSE203
Business Intelligence (BCA) - Fall 2024, Fall 2026 Slides
CSE22845
Applied Computing (M.Tech) - Fall 2024, Fall 2025, Fall 2026
TOL 4204
Flexible Automation and Artificial Intelligence (MSc) - Fall 2021, Fall 2022, NTNU Norway
Teaching
8 courses across B.Tech / M.Tech / BCA / PhD / MSc
Online
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Info

Contact (personal): dr.tamal.ghosh@outlook.com

Phone: +91-9163253061

© 2024 Dr. Tamal Ghosh.

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Introduction to Artificial Intelligence (CSE 11112) - Lecture Slides
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Lecture 1 AI Basics Lecture 1
AI Basics Lecture 2 Agents Lecture 2
Intelligent Agents Lecture 3 Problem Formulation Lecture 3
Problem Formulation Lecture 4 Uninformed Search Lecture 4
Uninformed Search Lecture 5 Informed Search Lecture 5
Informed Search Lecture 6 CSP Lecture 6
Constraint Satisfaction Lecture 7 GA Lecture 7
Genetic Algorithms Lecture 8 Planning Lecture 8
Planning Lecture 9 Planning with State Space Search Lecture 9
Planning with State Space Search Lecture 10 Partial Order Planning Lecture 10
Partial Order Planning Lecture 11 Planning Graph Lecture 11
Planning Graph Lecture 12 Propositional Logic Lecture 12
Propositional Logic Lecture 13 Inference Rules Lecture 13
Inference Rules Lecture 14 First-Order Logic Lecture 14
First-Order Logic Lecture 15 Inference in First-Order Logic Lecture 15
Inference in First-Order Logic Lecture 16 Unification and Resolution Lecture 16
Unification and Resolution Lecture 17 Forward and Backward Chaining Lecture 17
Forward and Backward Chaining Lecture 18 Reasoning and Uncertainty Lecture 18
Reasoning and Uncertainty Lecture 19 Bayes Theorem Lecture 19
Bayes Theorem Lecture 20 Markov Chain and Hidden MArkov Model Lecture 20
Markov Chain and Hidden MArkov Model Lecture 21 Fuzzy logic and Fuzzy Set Theory Lecture 21
Fuzzy logic and Fuzzy Set Theory Lecture 22 Learning ML DT Lecture 22
Learning ML DT Lecture 23 ANN Lecture 23
ANN
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Machine Learning (CSE 21816) - Lecture Slides
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Lecture 1 ML Basics Lecture 1
ML Basics: PAC Learning, KNN classification Lecture 2 Decision Tree Lecture 2
Decision Tree Lecture 3 Random Forest, Regression, Logistic Regression Lecture 3
Random Forest, Regression, Logistic Regression Lecture 4 CNN and RNN Lecture 4
CNN and RNN Lecture 5 Hands On Lecture 5
Hands On Lecture 6 Q&A Lecture 6
Q&A Lecture Notes Lecture 7
Lecture Notes
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Business Intelligence (CSE 203) - Lecture Slides
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Lecture 1 ML Basics Lecture 1
ML Basics: PAC Learning, KNN classification Lecture 2 Decision Tree Lecture 2
Decision Tree Lecture 3 Random Forest, Regression, Logistic Regression Lecture 3
Random Forest, Regression, Logistic Regression Lecture 4 CNN and RNN Lecture 4
CNN and RNN Lecture 5 Hands On Lecture 5
Hands On Lecture 6 Q&A Lecture 6
Q&A Lecture Notes Lecture 7
Lecture Notes