Course 2 Internship Program
Mentors & Curriculum
Unnati Nigam Researcher at IIT Bombay Topic: Time Series Analysis Session 1 Curriculum: Introduction to Time Series Analysis: Overview of key concepts and applications in time series analysis. Session 2 Curriculum: Statistical Models: Exploration of classical models like ARIMA for time series forecasting. Session 3 Curriculum: Seasonality and Trend Analysis: Study of methods to detect and analyze seasonal patterns and trends. Session 4 Curriculum: Non-Stationary Time Series: Examination of techniques for handling non-stationary data, including differencing and transformations. Session 5 Curriculum: Advanced Methods: Discussion of advanced techniques such as GARCH models and state space models. Session 6 Curriculum: Applications and Case Studies: Overview of real-world applications and case studies of time series analysis in various fields. Topic: Statistical Modelling and Forecasting Session 1 Curriculum: Introduction to Statistical Modelling: Overview of statistical models and their purpose in data analysis and forecasting. Session 2 Curriculum: Regression Analysis: Exploration of linear and nonlinear regression models for predicting outcomes. Session 3 Curriculum: Time Series Forecasting: Study of methods like ARIMA and exponential smoothing for forecasting temporal data. Session 4 Curriculum: Model Selection and Validation: Examination of techniques for selecting and validating statistical models, including cross-validation. Session 5 Curriculum: Advanced Forecasting Techniques: Discussion of advanced forecasting methods such as machine learning models and ensemble approaches. Session 6 Curriculum: Applications and Case Studies: Overview of real-world applications and case studies demonstrating the use of statistical modeling and forecasting. Topic: Probability Distributions and Hazard Functions Session 1 Curriculum: Introduction to Probability Distributions: Overview of probability distributions and their role in statistical analysis. Session 2 Curriculum: Discrete Distributions: Exploration of key discrete distributions like Binomial and Poisson. Session 3 Curriculum: Continuous Distributions: Study of important continuous distributions such as Normal, Exponential, and Gamma. Session 4 Curriculum: Hazard Functions: Examination of hazard functions and their use in survival analysis and reliability engineering. Session 5 Curriculum: Modeling with Distributions: Discussion of how different distributions and hazard functions are used in modeling and inference. Session 6 Curriculum: Applications and Case Studies: Overview of applications and real-world case studies involving probability distributions and hazard functions. Topic: Pandemic Data Modelling (COVID-19 Case Studies) Session 1 Curriculum: Introduction to Pandemic Data Modelling: Overview of the importance and methods of modeling pandemic data. Session 2 Curriculum: COVID-19 Case Models: Exploration of different models used to simulate and predict COVID-19 cases. Session 3 Curriculum: Parameter Estimation: Study of techniques for estimating model parameters and fitting them to COVID-19 data. Session 4 Curriculum: Model Validation: Examination of methods for validating and assessing the accuracy of pandemic models. Session 5 Curriculum: Scenario Analysis: Discussion of how different scenarios and interventions are modeled and analyzed in pandemic studies. Session 6 Curriculum: Applications and Policy Implications: Overview of how pandemic data modeling informs public health decisions and policy-making. Internship Milestones Session 1: - Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: - Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: - Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: - - Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: Statistical Techniques in Data Mining (Aug-Nov 2023) Linear Algebra and Ordinary Differential Equations (Jan-May 2024) International Summer School (June 2024): Statistical Machine Learning Probability I (Aug-Nov 2024)
Abhik Sarkar Researcher at IIT Guwahati Topic: Dark Matter Search at Future Collider Experiments. Session 1 Curriculum: Introduction to Dark Matter: Overview of dark matter and its significance in physics. Session 2 Curriculum: Collider Experiments: Exploration of how future colliders will search for dark matter. Session 3 Curriculum: Detection Techniques: Study of experimental methods for detecting dark matter interactions. Session 4 Curriculum: Theoretical Models: Examination of theoretical models predicting dark matter particles and their properties. Session 5 Curriculum: Experimental Challenges: Discussion of technical and conceptual challenges in dark matter searches. Session 6 Curriculum: Future Directions: Overview of anticipated advancements and next-generation collider experiments for dark matter research. Topic: Top Quark Studies at Future Collider Experiments. Session 1 Curriculum: Introduction to the Top Quark: Overview of the top quark’s role and significance in particle physics. Session 2 Curriculum: Collider Experiments: Exploration of how future colliders will study the top quark. Session 3 Curriculum: Top Quark Properties: Study of the top quark’s mass, decay modes, and interactions. Session 4 Curriculum: Theoretical Models: Examination of theoretical models related to the top quark, including its impact on the Standard Model. Session 5 Curriculum: Experimental Techniques: Discussion of techniques and technologies used to measure top quark properties. Session 6 Curriculum: Future Prospects: Overview of upcoming experiments and their potential to advance top quark research. Topic: Higgs Boson Studies at Future Collider Experiments. Session 1 Curriculum: Introduction to the Higgs Boson: Overview of the Higgs boson and its role in the Standard Model. Session 2 Curriculum: Collider Experiments: Exploration of how future colliders will investigate the Higgs boson. Session 3 Curriculum: Higgs Properties: Study of the Higgs boson’s mass, decay channels, and interactions. Session 4 Curriculum: Theoretical Models: Examination of theoretical extensions and implications of the Higgs boson. Session 5 Curriculum: Experimental Techniques: Discussion of methods used to measure and analyze the Higgs boson in collider experiments. Session 6 Curriculum: Future Directions: Overview of anticipated advancements and experiments aimed at probing the Higgs boson further. Topic: New Physics Models in the Light of LHC Higgs Data. Session 1 Curriculum: Introduction to New Physics Models: Overview of new physics concepts beyond the Standard Model. Session 2 Curriculum: LHC Higgs Data: Exploration of the Higgs boson data from the Large Hadron Collider (LHC). Session 3 Curriculum: Model Implications: Study of how LHC Higgs data impacts existing new physics models. Session 4 Curriculum: Beyond the Standard Model: Examination of extensions like supersymmetry, extra dimensions, and dark matter. Session 5 Curriculum: Theoretical Constraints: Discussion of constraints and challenges posed by Higgs data to new physics models. Session 6 Curriculum: Future Prospects: Overview of future experiments and theoretical developments to test new physics hypotheses. Topic: Monte Carlo Methods for Particle Physics and Beyond. Session 1 Curriculum: Introduction to Monte Carlo Methods: Overview of Monte Carlo methods and their importance in particle physics. Session 2 Curriculum: Simulation Techniques: Exploration of Monte Carlo techniques for simulating particle interactions and detector responses. Session 3 Curriculum: Event Generation: Study of event generators and their role in particle physics experiments. Session 4 Curriculum: Data Analysis: Examination of how Monte Carlo methods are used in analyzing experimental data. Session 5 Curriculum: Beyond Particle Physics: Application of Monte Carlo methods in other fields like astrophysics and medical physics. Session 6 Curriculum: Future Developments: Overview of advancements and future directions in Monte Carlo methods for particle physics and beyond. Topic: Stochastic Processes for Particle Physics and Beyond. Session 1 Curriculum: Introduction to Stochastic Processes: Overview of stochastic processes and their relevance to particle physics. Session 2 Curriculum: Particle Interaction Modeling: Exploration of stochastic methods for modeling particle interactions and decay processes. Session 3 Curriculum: Detector Response: Study of stochastic processes in the context of detector response and signal processing. Session 4 Curriculum: Statistical Analysis: Examination of statistical techniques and their application to experimental data analysis. Session 5 Curriculum: Beyond Particle Physics: Application of stochastic processes in fields like finance, biology, and engineering. Session 6 Curriculum: Future Directions: Overview of advancements and emerging applications of stochastic processes in particle physics and other domains. Topic: Supervised Machine Learning for Collider Studies. Session 1 Curriculum: Introduction to Supervised Machine Learning: Overview of supervised machine learning techniques and their relevance to collider studies. Session 2 Curriculum: Data Classification: Exploration of classification algorithms for identifying particle events and anomalies. Session 3 Curriculum: Feature Extraction: Study of methods for extracting relevant features from collider data. Session 4 Curriculum: Model Training: Examination of training supervised models with collider experiment data. Session 5 Curriculum: Performance Evaluation: Discussion of techniques for evaluating model performance and accuracy. Session 6 Curriculum: Future Applications: Overview of potential advancements and applications of supervised machine learning in future collider experiments. Topic: Unsupervised Machine Learning for Collider Studies. Session 1 Curriculum: Introduction to Unsupervised Machine Learning: Overview of unsupervised learning techniques and their applications in collider studies. Session 2 Curriculum: Clustering Techniques: Exploration of clustering algorithms for grouping particle events and identifying patterns. Session 3 Curriculum: Dimensionality Reduction: Study of methods like PCA and t-SNE for reducing data complexity and visualizing high-dimensional collider data. Session 4 Curriculum: Anomaly Detection: Examination of unsupervised methods for detecting unusual or novel particle interactions. Session 5 Curriculum: Data Visualization: Discussion of visualization techniques for interpreting results from unsupervised learning algorithms. Session 6 Curriculum: Future Directions: Overview of emerging trends and potential advancements in unsupervised machine learning for collider research. Internship Milestones: Session 1: Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: https://in.linkedin.com/in/a2sabhik?original_referer=https%3A%2F%2Fwww.google.com%2F
Likun Pradhan Researcher at IIT Guwahati Topic: Computational Physics Session 1 Curriculum: Introduction to Computational Physics: Overview of computational methods and their role in solving physical problems. Session 2 Curriculum: Numerical Methods: Study of key numerical techniques like finite difference and Monte Carlo methods. Session 3 Curriculum: Simulations: Exploration of physical simulations, including molecular dynamics and N-body simulations. Session 4 Curriculum: Quantum Computation: Examination of computational approaches to quantum mechanics problems. Session 5 Curriculum: Data Analysis: Analysis of large datasets in physics using computational tools. Session 6 Curriculum: Applications: Discussion of real-world applications of computational physics in research and industry. Internship Milestone: Session 1: Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: https://in.linkedin.com/in/likun-pradhan-769292210
Sanjeev Sikri Researcher at IIT Kanpur Topic: Introduction to Indian Philosophy (Basic and Advanced) Session 1 Curriculum: Overview of Indian Philosophy: Introduction to the key schools of Indian philosophical thought. Session 2 Curriculum: Basic Concepts: Fundamental concepts like Dharma, Karma, and Moksha across different traditions. Session 3 Curriculum: Vedanta Philosophy: Core principles of Advaita, Vishishtadvaita, and Dvaita Vedanta. Session 4 Curriculum: Buddhist Philosophy: Basic and advanced teachings of Buddhism, including Madhyamaka and Yogachara. Session 5 Curriculum: Jain and Sikh Philosophies: Key tenets of Jainism and Sikhism and their contributions to Indian philosophy. Session 6 Curriculum: Comparative Analysis: Comparative study of Indian philosophies and their influence on global thought. Topic: Introduction to Ancient Greek Philosophy Session 1 Curriculum: Overview of Greek Philosophy: Introduction to key figures and periods of ancient Greek philosophy. Session 2 Curriculum: Pre-Socratic Philosophers: Exploration of early thinkers like Thales, Heraclitus, and Pythagoras. Session 3 Curriculum: Socrates: Examination of Socratic method and its impact on Western thought. Session 4 Curriculum: Plato: Study of Plato’s theories of forms, knowledge, and the ideal state. Session 5 Curriculum: Aristotle: Analysis of Aristotle’s contributions to logic, ethics, and natural sciences. Session 6 Curriculum: Hellenistic Philosophies: Overview of Stoicism, Epicureanism, and Skepticism and their influence. Topic: Introduction to Existentialism Session 1 Curriculum: Overview of Existentialism: Introduction to the key themes and origins of existentialist philosophy. Session 2 Curriculum: Kierkegaard: Examination of Søren Kierkegaard’s ideas on faith, despair, and individuality. Session 3 Curriculum: Nietzsche: Study of Friedrich Nietzsche’s concepts of the Übermensch, nihilism, and the will to power. Session 4 Curriculum: Heidegger: Exploration of Martin Heidegger’s notions of Being, authenticity, and existential anxiety. Session 5 Curriculum: Sartre: Analysis of Jean-Paul Sartre’s ideas on freedom, bad faith, and existential responsibility. Session 6 Curriculum: Camus: Discussion of Albert Camus’ philosophy of the absurd and the concept of revolt. Topic: Philosophy of Friendship Session 1 Curriculum: Introduction to the Philosophy of Friendship: Overview of key themes and questions surrounding friendship in philosophy. Session 2 Curriculum: Aristotle on Friendship: Examination of Aristotle’s three types of friendship and their ethical significance. Session 3 Curriculum: Cicero and Roman Views: Study of Cicero’s perspectives on friendship as a moral and social bond. Session 4 Curriculum: Medieval and Renaissance Thought: Exploration of friendship in the context of religious and humanistic philosophy. Session 5 Curriculum: Modern Philosophical Views: Analysis of modern perspectives on friendship, including Kant, Nietzsche, and Emerson. Session 6 Curriculum: Contemporary Debates: Discussion of contemporary philosophical debates on the role of friendship in society and ethics. Topic: Introduction to Western Ethics Session 1 Curriculum: Overview of Western Ethics: Introduction to the main branches and historical development of Western ethical thought. Session 2 Curriculum: Ancient Ethics: Study of virtue ethics through Plato, Aristotle, and Stoicism. Session 3 Curriculum: Medieval Ethics: Exploration of Christian ethics with focus on Augustine and Aquinas. Session 4 Curriculum: Modern Ethics: Analysis of deontological ethics by Kant and consequentialism by Bentham and Mill. Session 5 Curriculum: 19th-Century Ethics: Examination of moral philosophy in Nietzsche’s critique of traditional ethics. Session 6 Curriculum: Contemporary Ethics: Discussion of contemporary issues in ethics, including relativism, feminist ethics, and applied ethics. Topic: Introduction to Indian Ethics Session 1 Curriculum: Overview of Indian Ethics: Introduction to the foundational concepts and traditions in Indian ethical thought. Session 2 Curriculum: Vedic and Upanishadic Ethics: Exploration of Dharma, Karma, and Moksha in early Indian texts. Session 3 Curriculum: Buddhist Ethics: Study of the Eightfold Path and the ethical teachings of Buddhism. Session 4 Curriculum: Jain Ethics: Examination of Ahimsa, non-attachment, and other key principles in Jainism. Session 5 Curriculum: Hindu Ethics: Analysis of ethical teachings in the Bhagavad Gita and other Hindu scriptures. Session 6 Curriculum: Contemporary Indian Ethics: Discussion of modern interpretations and applications of Indian ethical principles in today’s society. Topic: Rationalists and Empiricists In Western Philosophy Session 1 Curriculum: Introduction to Rationalism and Empiricism: Overview of the key differences and themes in rationalist and empiricist thought. Session 2 Curriculum: Descartes: Study of René Descartes’ rationalism, focusing on doubt, cogito, and innate ideas. Session 3 Curriculum: Spinoza and Leibniz: Exploration of Spinoza’s monism and Leibniz’s concept of pre-established harmony in rationalist philosophy. Session 4 Curriculum: Locke: Examination of John Locke’s empiricism, including the theory of tabula rasa and the nature of knowledge. Session 5 Curriculum: Berkeley and Hume: Analysis of Berkeley’s idealism and Hume’s skepticism within the empiricist tradition. Session 6 Curriculum: Kant’s Synthesis: Discussion of Immanuel Kant’s attempt to bridge rationalism and empiricism with his critical philosophy. Topic: Epistemology: Indian and Western Session 1 Curriculum: Introduction to Epistemology: Overview of key questions and themes in Indian and Western epistemology. Session 2 Curriculum: Pramana in Indian Philosophy: Study of the means of knowledge (Pramana) in Indian traditions like Nyaya and Vedanta. Session 3 Curriculum: Western Rationalism: Exploration of knowledge through reason in Western philosophy, focusing on Descartes and Leibniz. Session 4 Curriculum: Western Empiricism: Examination of knowledge through experience in Western thought, with emphasis on Locke, Berkeley, and Hume. Session 5 Curriculum: Buddhist Epistemology: Analysis of knowledge and perception in Buddhist philosophy, including theories of momentariness and emptiness. Session 6 Curriculum: Kant and Indian Comparisons: Discussion of Kant’s critical philosophy and its parallels with Indian epistemological theories. Topic: Introduction to Philosophy of Religion Session 1 Curriculum: Overview of Philosophy of Religion: Introduction to key questions and themes in the philosophy of religion. Session 2 Curriculum: Arguments for the Existence of God: Examination of classical arguments like the Ontological, Cosmological, and Teleological arguments. Session 3 Curriculum: Problem of Evil: Exploration of the problem of evil and responses from theodicy. Session 4 Curriculum: Faith and Reason: Analysis of the relationship between faith and reason in religious belief. Session 5 Curriculum: Religious Experience: Study of the nature and significance of religious experiences. Session 6 Curriculum: Pluralism and Religious Diversity: Discussion of philosophical approaches to religious pluralism and diversity. Internship Milestone: Session 1: Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: https://in.linkedin.com/in/sanjeev-sikri-61696964?original_referer=https%3A%2F%2Fwww.google.com%2F
Tushar Dhamale Researcher at IIT Bombay Topic: Enzymatic mechanisms in bacterial pollutant degradation Session 1 Curriculam: Introduction to Enzymes: Basic principles of enzymatic catalysis and specificity. Session 2 Curriculam: Bacterial Enzymes: Key bacterial enzymes involved in pollutant degradation. Session 3 Curriculam: Enzyme Mechanisms: Detailed mechanisms of enzyme action in pollutant breakdown. Session 4 Curriculam: Pathways of Degradation: Overview of metabolic pathways for pollutant degradation in bacteria. Session 5 Curriculam: Enzyme Engineering: Strategies for enhancing bacterial enzyme efficiency in degradation. Session 6 Curriculam: Applications: Real-world applications of bacterial enzymes in environmental cleanup. Topic: Transporter proteins and their roles in bacterial pollutant uptake Session 1 Curriculam: Introduction to Transporter Proteins: Basic structure and function of bacterial transporter proteins. Session 2 Curriculam: Types of Transporters: Classification and types of transporters involved in pollutant uptake. Session 3 Curriculam: Mechanisms of Uptake: Detailed mechanisms by which transporter proteins facilitate pollutant uptake. Session 4 Curriculam: Regulation of Transporters: How bacterial cells regulate transporter activity in response to pollutants. Session 5 Curriculam: Transporter Engineering: Approaches to modify transporters for enhanced pollutant uptake. Session 6 Curriculam: Applications: Utilization of transporter proteins in bioremediation and pollutant removal. Topic: Genome mining for pollutant degradation pathways in bacteria. Session 1 Curriculam: Introduction to Genome Mining: Overview of genome mining and its relevance to pollutant degradation Session 2 Curriculam: Bioinformatics Tools: Key tools and techniques for identifying degradation pathways in bacterial genomes. Session 3 Curriculam: Gene Clusters: Identification and analysis of gene clusters involved in pollutant degradation. Session 4 Curriculam: Pathway Prediction: Predicting metabolic pathways for pollutant degradation using genomic data. Session 5 Curriculam: Functional Validation: Experimental approaches to validate predicted degradation pathways. Session 6 Curriculam: Applications: Applying genome mining to discover new bacterial strains for bioremediation Topic: Comparative analysis of bacterial plastic degradation enzymes Session 1 Curriculam: Introduction to Plastic Degradation: Overview of bacterial enzymes involved in plastic degradation. Session 2 Curriculam: Types of Enzymes: Classification and types of enzymes that degrade different plastics. Session 3 Curriculam: Comparative Mechanisms: Analysis of how different bacterial enzymes degrade plastics. Session 4 Curriculam: Structural Comparison: Structural differences and similarities among plastic-degrading enzymes. Session 5 Curriculam: Evolutionary Insights: Evolutionary analysis of plastic degradation enzymes across bacterial species. Session 6 Curriculam: Applications: Using comparative analysis to enhance plastic degradation through enzyme engineering. Topic: Phylogenetic methods for bacterial classification Session 1 Curriculam: Introduction to Phylogenetics: Overview of phylogenetic methods and their importance in bacterial classification. Session 2 Curriculam: Molecular Markers: Key molecular markers used in phylogenetic analysis. Session 3 Curriculam: Tree Construction: Methods for constructing phylogenetic trees to classify bacteria. Session 4 Curriculam: Sequence Alignment: Techniques for aligning genetic sequences in phylogenetic studies. Session 5 Curriculam: Evolutionary Models: Application of evolutionary models in phylogenetic analysis. Session 6 Curriculam: Applications: Use of phylogenetic methods in understanding bacterial diversity and taxonomy. Topic: Gene annotation and evolutionary analysis of bacteriophages Session 1 Curriculam: Introduction to Gene Annotation: Overview of gene annotation in bacteriophages. Session 2 Curriculam: Bioinformatics Tools: Key tools used for annotating bacteriophage genomes. Session 3 Curriculam: Functional Annotation: Identifying and assigning functions to bacteriophage genes. Session 4 Curriculam: Comparative Genomics: Comparing annotated genes across different bacteriophages. Session 5 Curriculam: Evolutionary Analysis: Studying the evolution of bacteriophage genes and their diversity. Session 6 Curriculam: Applications: Applying gene annotation and evolutionary analysis to bacteriophage research and therapy. Topic: Identification of pollutant-degrading gene clusters in bacteria. Session 1 Curriculum: Introduction to Gene Clusters: Overview of pollutant-degrading gene clusters in bacteria. Session 2 Curriculum: Identification Techniques: Methods for identifying gene clusters involved in pollutant degradation. Session 3 Curriculum: Functional Analysis: Analyzing the function of genes within pollutant-degrading clusters. Session 4 Curriculum: Genomic Context: Studying the genomic context and organization of these gene clusters. Session 5 Curriculum: Comparative Analysis: Comparing pollutant-degrading gene clusters across different bacterial species. Session 6 Curriculum: Applications: Utilizing identified gene clusters for bioremediation and environmental cleanup. Topic: Metabolic diversity in bacterial pollutant degradation. Session 1 Curriculum: Introduction to Metabolic Diversity: Overview of metabolic diversity in bacterial pollutant degradation. Session 2 Curriculum: Metabolic Pathways: Key metabolic pathways used by bacteria to degrade pollutants. Session 3 Curriculum: Enzyme Diversity: Diversity of enzymes involved in pollutant degradation across bacterial species. Session 4 Curriculum: Genomic Insights: Genomic approaches to understanding metabolic diversity in degradation processes. Session 5 Curriculum: Environmental Factors: Influence of environmental conditions on metabolic diversity and efficiency. Session 6 Curriculum: Applications: Leveraging metabolic diversity for improved bioremediation strategies. Internship Milestone: Session 1: Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: A science enthusiast looking forward for opportunities to learn, explore and experience new challenges in science and life.
Iman Sengupta Researcher at IIT Kharagpur Topic: Statistical Inference and Analysis Session 1 Curriculum: Introduction to Statistical Inference: Overview of statistical inference methods and their purpose in data analysis. Session 2 Curriculum: Point Estimation: Exploration of techniques for estimating population parameters from sample data. Session 3 Curriculum: Hypothesis Testing: Study of methods for testing hypotheses and making inferences about data. Session 4 Curriculum: Confidence Intervals: Examination of how to construct and interpret confidence intervals for parameter estimates. Session 5 Curriculum: Regression Analysis: Discussion of regression models and their role in understanding relationships between variables. Session 6 Curriculum: Advanced Techniques: Overview of advanced statistical inference methods, including Bayesian inference and non-parametric approaches. Topic: Machine Learning and Data Mining Session 1 Curriculum: Introduction to Machine Learning and Data Mining: Overview of the core concepts and differences between machine learning and data mining. Session 2 Curriculum: Data Preparation: Exploration of techniques for cleaning, transforming, and preparing data for analysis. Session 3 Curriculum: Machine Learning Algorithms: Study of key algorithms, including supervised and unsupervised learning methods. Session 4 Curriculum: Data Mining Techniques: Examination of techniques for discovering patterns and relationships in large datasets. Session 5 Curriculum: Model Evaluation: Discussion of methods for evaluating the performance of machine learning and data mining models. Session 6 Curriculum: Applications and Case Studies: Overview of real-world applications and case studies demonstrating the use of machine learning and data mining. Topic: Econometrics Session 1 Curriculum: Introduction to Econometrics: Overview of econometrics and its role in economic data analysis. Session 2 Curriculum: Regression Analysis: Exploration of linear and nonlinear regression techniques for modeling economic relationships Session 3 Curriculum: Time Series Econometrics: Study of methods for analyzing and forecasting economic time series data. Session 4 Curriculum: Panel Data Analysis: Examination of techniques for analyzing data that involves multiple entities over time. Session 5 Curriculum: Instrumental Variables: Discussion of methods for dealing with endogeneity and using instrumental variables. Session 6 Curriculum: Advanced Topics: Overview of advanced econometric methods, including structural equation modeling and Bayesian econometrics. Topic: Time Series Analysis Session 1 Curriculum: Introduction to Time Series Analysis: Overview of time series analysis concepts and applications. Session 2 Curriculum: Time Series Components: Exploration of trend, seasonality, and noise in time series data. Session 3 Curriculum: Stationarity and Transformation: Study of techniques for handling non-stationary data, including differencing and transformation. Session 4 Curriculum: ARIMA Models: Examination of Autoregressive Integrated Moving Average (ARIMA) models for forecasting. Session 5 Curriculum: Seasonal Models: Discussion of models like SARIMA for handling seasonal effects in time series data. Session 6 Curriculum: Advanced Techniques: Overview of advanced methods, such as GARCH models and state space models, for complex time series analysis. Topic: Applied Statistical Modeling Session 1 Curriculum: Introduction to Applied Statistical Modeling: Overview of the role and techniques of statistical modeling in real-world applications. Session 2 Curriculum: Model Building: Exploration of methods for constructing statistical models based on data and research questions. Session 3 Curriculum: Regression Techniques: Study of various regression models, including linear, logistic, and polynomial regressions. Session 4 Curriculum: Model Diagnostics: Examination of techniques for diagnosing and improving model performance and fit. Session 5 Curriculum: Handling Complex Data: Discussion of methods for dealing with complex datasets, such as high-dimensional data and mixed models. Session 6 Curriculum: Case Studies and Applications: Overview of real-world applications and case studies demonstrating the use of statistical modeling in various fields. Internship Milestones Session 1: - Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: - Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: - Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: - - Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: Worked on the large-scale continuous production of silver nanoparticles and related product development. Have research expertise in the synthesis of novel nanomaterials like graphene, Layered Double Hydroxides (LDH), and their applications in industrial heat transfer, biomedical applications. Have profound knowledge and experience in various materials characterization techniques.
Harish Kumar Trivedi Researcher at IIT Kanpur Topic: Quantum Chemistry: Advanced Perturbation Theory and its application to molecular systems. Session 1 Curriculum: Introduction to Perturbation Theory: Overview of perturbation theory in quantum chemistry and its importance. Session 2 Curriculum: First-Order Perturbation Theory: Exploration of first-order corrections to energy levels and wavefunctions Session 3 Curriculum: Second-Order Perturbation Theory: Study of second-order perturbation corrections and their applications. Session 4 Curriculum: Application to Molecular Systems: Examination of how perturbation theory is applied to analyze molecular systems and interactions. Session 5 Curriculum: Multi-Reference Perturbation Theory: Discussion of advanced perturbation methods for systems with multiple reference states. Session 6 Curriculum: Case Studies and Applications: Overview of real-world examples and applications of perturbation theory in understanding molecular properties and reactions. Topic: Statistical Thermodynamics: Partition functions and their role in determining thermodynamic properties. Session 1 Curriculum: Introduction to Statistical Thermodynamics: Overview of statistical thermodynamics and its relationship with classical thermodynamics. Session 2 Curriculum: Partition Functions: Exploration of partition functions and their significance in statistical mechanics. Session 3 Curriculum: Calculating Thermodynamic Properties: Study of how partition functions are used to determine properties like free energy, entropy, and internal energy. Session 4 Curriculum: Canonical Ensemble: Examination of the canonical partition function and its role in systems at constant temperature. Session 5 Curriculum: Grand Canonical Ensemble: Discussion of the grand canonical partition function and its application to systems with variable particle numbers. Session 6 Curriculum: Applications and Examples: Overview of practical examples and applications of partition functions in understanding molecular and macroscopic thermodynamic properties. Topic: Advanced Organic Chemistry: Mechanisms of pericyclic reactions and their applications. Session 1 Curriculum: Introduction to Pericyclic Reactions: Overview of pericyclic reactions and their significance in organic chemistry. Session 2 Curriculum: Electrocyclic Reactions: Exploration of mechanisms and rules governing electrocyclic reactions. Session 3 Curriculum: Cycloadditions: Study of cycloaddition reactions, including Diels-Alder reactions and their mechanistic details. Session 4 Curriculum: Sigmatropic Rearrangements: Examination of sigmatropic rearrangements and their reaction mechanisms. Session 5 Curriculum: Application to Synthesis: Discussion of how pericyclic reactions are applied in synthetic organic chemistry. Session 6 Curriculum: Case Studies and Examples: Overview of real-world examples and applications of pericyclic reactions in complex organic synthesis. Topic: Spectroscopy: Application of NMR and mass spectrometry in structure determination. Session 1 Curriculum: Introduction to Spectroscopy: Overview of spectroscopy techniques and their importance in structure determination. Session 2 Curriculum: Nuclear Magnetic Resonance (NMR): Exploration of NMR principles and its application in identifying molecular structure Session 3 Curriculum: Mass Spectrometry (MS): Study of mass spectrometry techniques and their use in determining molecular weights and structural features. Session 4 Curriculum: Combining NMR and MS: Examination of how NMR and MS are used together for comprehensive structural analysis. Session 5 Curriculum: Advanced NMR Techniques: Discussion of advanced NMR methods like 2D NMR for complex structure elucidation. Session 6 Curriculum: Case Studies and Applications: Overview of real-world examples and applications of NMR and MS in identifying and characterizing molecular structures. Topic: Introduction to Catalytic Cycles: Overview of catalytic cycles and their significance in organometallic chemistry. Session 1 Curriculum: Introduction to Catalytic Cycles: Overview of catalytic cycles and their significance in organometallic chemistry. Session 2 Curriculum: Basic Mechanisms: Exploration of fundamental mechanisms in organometallic catalytic cycles Session 3 Curriculum: Oxidative Addition and Reductive Elimination: Study of key steps like oxidative addition and reductive elimination in catalytic processes. Session 4 Curriculum: Ligand Substitution: Examination of ligand exchange processes and their role in catalytic cycles Session 5 Curriculum: Applications in Synthesis: Discussion of how organometallic catalytic cycles are applied in industrial and laboratory synthesis. Session 6 Curriculum: Case Studies and Examples: Overview of real-world examples and applications of catalytic cycles in organometallic chemistry. Topic: Materials Chemistry: Recent advancements in nanomaterials and their applications. Session 1 Curriculum: Introduction to Nanomaterials: Overview of nanomaterials and their unique properties. Session 2 Curriculum: Synthesis Methods: Exploration of recent advancements in techniques for synthesizing nanomaterials. Session 3 Curriculum: Characterization Techniques: Study of methods for characterizing nanomaterials, including imaging and spectroscopy. Session 4 Curriculum: Applications in Electronics: Examination of how nanomaterials are used in electronic devices and technologies. Session 5 Curriculum: Applications in Medicine: Discussion of the role of nanomaterials in medical applications, including drug delivery and imaging. Session 6 Curriculum: Future Trends: Overview of emerging trends and future directions in nanomaterials research and applications. Internship Milestones Session 1: - Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: - Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: - Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: - - Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: Chemical professional with more than 12 years experience in chemical analysis, R&D, and production management, expertise in operating laboratory instruments and classical analysis, GATE qualified in chemistry AIR 951, material specialist, Expertise in the recycling of industrial waste and recycling of electronics materials of photovoltaic modules and supercapacitor. Able to implement and improve 5S and six sigma in the organization.
Anuj Birani Researcher at IIT BOMBAY Topic: Machine Learning Foundations Session 1 Curriculum: Introduction to Machine Learning Foundations: Overview of the fundamental concepts and theoretical underpinnings of machine learning. Session 2 Curriculum: Linear Algebra and Calculus: Exploration of essential mathematical tools like matrices, vectors, and derivatives used in machine learning. Session 3 Curriculum: Probability and Statistics: Study of probabilistic models, distributions, and statistical methods in machine learning. Session 4 Curriculum: Optimization Techniques: Examination of optimization algorithms like gradient descent for model training. Session 5 Curriculum: Learning Theory: Discussion of key concepts like bias-variance tradeoff, overfitting, and generalization. Session 6 Curriculum: Evaluation Metrics: Overview of methods to evaluate model performance, such as accuracy, precision, recall, and F1 score. Topic: Deep Learning for NLP Session 1 Curriculum: Introduction to Deep Learning for NLP: Overview of deep learning techniques applied to natural language processing. Session 2 Curriculum: Word Embeddings: Exploration of word vector representations like Word2Vec and GloVe. Session 3 Curriculum: Recurrent Neural Networks (RNNs): Study of RNNs and their applications in sequence modeling for NLP tasks. Session 4 Curriculum: Transformers and Attention Mechanism: Examination of transformer models and attention mechanisms in NLP. Session 5 Curriculum: Pre-trained Models: Discussion of models like BERT and GPT for transfer learning in NLP. Session 6 Curriculum: Applications: Overview of deep learning applications in NLP, including translation, sentiment analysis, and text generation. Topic: Mathematical Optimization Techniques Session 1 Curriculum: Introduction to Mathematical Optimization: Overview of optimization techniques and their applications in various fields. Session 2 Curriculum: Linear Programming: Exploration of methods for solving linear optimization problems, including the Simplex method. Session 3 Curriculum: Nonlinear Optimization: Study of techniques for handling nonlinear objective functions and constraints. Session 4 Curriculum: Gradient-Based Methods: Examination of gradient descent and its variants for optimization. Session 5 Curriculum: Convex Optimization: Discussion of convex sets and functions, and their role in optimization problems. Session 6 Curriculum: Combinatorial Optimization: Overview of methods like dynamic programming and branch-and-bound for discrete optimization problems. Topic: Simulation Modeling and Analysis Session 1 Curriculum: Introduction to Simulation Modeling: Overview of simulation modeling concepts and its applications in system analysis. Session 2 Curriculum: Discrete-Event Simulation: Exploration of techniques for simulating systems with distinct events over time. Session 3 Curriculum: Session 4 Curriculum: Monte Carlo Simulation: Examination of Monte Carlo methods for probabilistic and statistical simulation. Session 5 Curriculum: Validation and Verification: Discussion of techniques for validating and verifying simulation models. Session 6 Curriculum: Performance Analysis: Overview of tools and methods for analyzing simulation outcomes and system performance. Topic: Quantitative Models for Supply Chain Management Session 1 Curriculum: Introduction to Quantitative Models in Supply Chain: Overview of quantitative techniques for optimizing supply chain processes. Session 2 Curriculum: Inventory Management Models: Exploration of EOQ and safety stock models for efficient inventory control. Session 3 Curriculum: Demand Forecasting: Study of quantitative forecasting techniques like time series analysis and regression. Session 4 Curriculum: Network Design: Examination of optimization models for supply chain network design and facility location. Session 5 Curriculum: Transportation Models: Discussion of methods for optimizing transportation and logistics in supply chains. Session 6 Curriculum: Internship Milestones Session 1: - Introduction and Goal Setting - Overview of the internship objectives. - Introduction to the key concepts and tools relevant to the topic. - Setting personal learning goals and understanding expected outcomes. - Initial hands-on activity or project setup. Session 2: - Intermediate Application and Exploration - Deep dive into core concepts with practical examples. - Guided application of learned concepts through a focused project or task. - Mid-session check-in on progress and troubleshooting challenges. - Introduction to additional tools or methodologies to enhance understanding. Session 3: - Advanced Techniques and Problem-Solving - Exploration of advanced concepts and techniques. - Problem-solving sessions or case studies related to the topic. - Individual or group project work with real-world applications. - Feedback on progress and refinement of approaches. Session 4: - - Final Project and Reflection - Completion of the final project, integrating all learned concepts. - Presentation of findings or outcomes from the project. - Reflective discussion on the learning experience and challenges faced. - Evaluation and feedback session, focusing on areas for future improvement. About Educator: I am M.Tech (Research Assistant) student at Industrial Engineering and Operations Research, IIT Bombay. I am passionate about my work and always eager to learn new things and to connect with other fellow learners. I am skilled at Machine Learning, Deep Learning, Operations Research and Linux.
Course 2 Intern
